Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here. We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you'll be empowered to envision its most powerful applications, and to make them happen. About the team Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty. Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work up to three days per week on site with these customers which may require travel to locations throughout the UK. When not required on client sites, you'll have the flexibility to work from our London office or remotely from elsewhere within the UK. About the role Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production grade ML systems. What you'll be doing: Building and deploying production grade ML software, tools, and infrastructure. Creating reusable, scalable solutions that accelerate the delivery of ML systems. Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges. Leading technical scoping and architectural decisions to ensure project feasibility and impact. Defining and implementing Faculty's standards for deploying machine learning at scale. Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders. Who we're looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit learn, TensorFlow, or PyTorch. You possess strong Python skills and solid experience in software engineering best practices. You bring hands on experience with cloud platforms and infrastructure (e.g. AWS, Azure, GCP), including architecture and security. You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale. You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. You're an excellent communicator, able to guide technical teams and confidently advise non technical stakeholders. You thrive in a fast paced environment, and enjoy the autonomy to own scope, solve and deliver solutions. Our Interview Process Talent Team Screen (30 minutes) Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes) Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. Some of our standout benefits: Unlimited Annual Leave Policy Private healthcare and dental Enhanced parental leave Family Friendly Flexibility & Flexible working Sanctus Coaching Hybrid Working (2 days in our Old Street office, London) If you don't feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part time hours.
23/05/2026
Full time
Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here. We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you'll be empowered to envision its most powerful applications, and to make them happen. About the team Our Defence team is focused on building and embedding human-centered AI solutions which give our nation a competitive edge in the defence sector. We collaborate with our clients to bring ethical, reliable and cutting-edge AI to high-stakes situations and maintain the balance of global powers essential to our liberty. Because of the nature of the work we do with our Defence clients, you will need to be eligible for UK Security Clearance (SC) and willing to work up to three days per week on site with these customers which may require travel to locations throughout the UK. When not required on client sites, you'll have the flexibility to work from our London office or remotely from elsewhere within the UK. About the role Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production grade ML systems. What you'll be doing: Building and deploying production grade ML software, tools, and infrastructure. Creating reusable, scalable solutions that accelerate the delivery of ML systems. Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges. Leading technical scoping and architectural decisions to ensure project feasibility and impact. Defining and implementing Faculty's standards for deploying machine learning at scale. Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders. Who we're looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit learn, TensorFlow, or PyTorch. You possess strong Python skills and solid experience in software engineering best practices. You bring hands on experience with cloud platforms and infrastructure (e.g. AWS, Azure, GCP), including architecture and security. You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale. You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. You're an excellent communicator, able to guide technical teams and confidently advise non technical stakeholders. You thrive in a fast paced environment, and enjoy the autonomy to own scope, solve and deliver solutions. Our Interview Process Talent Team Screen (30 minutes) Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes) Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. Some of our standout benefits: Unlimited Annual Leave Policy Private healthcare and dental Enhanced parental leave Family Friendly Flexibility & Flexible working Sanctus Coaching Hybrid Working (2 days in our Old Street office, London) If you don't feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part time hours.
Bind Research is an innovative not-for-profit research organisation at the forefront of developing tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics - with a strong focus on nuclear magnetic resonance (NMR) spectroscopy - as well as computational approaches and cellular studies. You will play a crucial role in shaping the future of this cutting-edge research initiative. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins. If you are dedicated to supporting the advancement science and technology in an innovative environment, we encourage you to apply! Role Overview We are seeking a Machine Learning Engineer to advance data-processing and model-building and deployment capabilities at Bind. This role includes developing new machine-learning models for highly complex and heterogeneous scientific data such as from nuclear magnetic resonance (NMR), deploying and productionizing these models internally and externally, contributing to open-source software, and large-scale data analysis, curation, and pipeline building. Key Responsibilities Develop innovative machine learning approaches to elucidate and quantify the interactions between small molecules and intrinsically disordered proteins Integrate molecular simulations and deep learning approaches using cutting-edge architectures Software Engineering Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching systems for internal and external use Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation Assist in optimising data collection practices in both computational and experimental teams Mentor and support Bind's interdisciplinary team in machine-learning and data analysis methods Driving Innovation Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies Contribute to the design and execution of cutting-edge machine learning and NMR research projects that advance Bind's scientific mission Thrive in a dynamic, start-up-style environment where initiative and flexibility in your role are valued. Qualifications and Expertise We encourage applications from software engineers, scientists, and individuals with relevant transferable skills who are enthusiastic about our mission to make disordered proteins druggable, even if they do not meet every requirement listed below. We believe innovation thrives through diverse perspectives and welcome candidates from both academic and industry backgrounds. Education and Experience MSc in a technical field with 3 years of machine learning or model-building experience or a PhD with a similar focus Extensive knowledge of machine learning approaches, neural network architectures, training methods, and data preparation best practices Experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series Strong dev-ops skillset, with proficiency in model deployment, versioning, distributed architectures, and containerization Track record of completed scientific software projects or open-source project contributions Skills and Abilities Strong written and verbal communication skills, with the ability to communicate effectively with team members in diverse fields Strong programming abilities in Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL Expertise with deep learning approaches such as diffusion or flow matching Proficiency in modern software development practices: code testing, documentation, packaging and deployment, version control using Git, containerization Proven ability to process, analyse, and present large and complex datasets using techniques such as clustering and dimensionality reduction Additional Attributes A collaborative mindset and an enthusiasm for interdisciplinary teamwork A strong engineering mindset: you believe ease-of-use, reproducibility, maintainability, and clear documentation are key requirements for scientific software and allow complex projects to gain results faster Dedication to continuous professional development in machine learning, dev-ops, programming, and a willingness to learn more about experimental biophysical methods Passion for contributing to the establishment and growth of a world-class not-for-profit research organisation Nice to Have Knowledge of NMR spectroscopy and associated data processing pipelines Familiarity with simulation techniques such as molecular dynamics or Monte Carlo approaches, as well as an understanding of statistical mechanics and complex systems Ability to use HPC and / or cloud computing and building automation and orchestration systems for these platforms Proficiency in a low-level language such as C, C++, or Rust and in GPU frameworks like CUDA Competence in front-end web design to allow easy interfacing with large datasets Our Culture Follow the science. We prioritise rigorous scientific inquiry, relying on evidence and expertise to guide decisions and actions, incorporating the latest research to achieve meaningful, ethical, and impactful outcomes for the public and scientific community. Think dynamically. We believe the most effective solutions come from a dynamic, adaptable mindset that embraces uncertainty as a catalyst for discovery, encouraging creativity, challenging assumptions, and approaching problems from multiple angles to foster innovation, navigate complexity, and deliver exceptional results. Celebrate a diverse ensemble. We celebrate diversity and inclusion, fostering a culture where all perspectives, backgrounds, and talents are valued, respected, and empowered to thrive, enabling us to better understand our community, collaborate effectively, and deliver impactful solutions. Build an innovation hub. We strive to advance disordered protein research by creating and sharing tools and datasets collaboratively, building on past contributions, and working alongside the disordered protein community to deepen understanding and maximise collective impact. What We Offer 38 days holiday (inclusive of bank holidays) Employer pension contribution in line with market standards Cycle to work scheme Life insurance Additional information The interview process will begin with a phone screen. Successful candidates will then be invited to more comprehensive technical and cultural interviews. To apply send your CV and cover letter to with the reference number BRJ017 and your name in the email header. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins! We are committed to protecting your personal information. For full details on how we collect, use, and store your data during the recruitment process, please refer to our Privacy Notice .
23/05/2026
Full time
Bind Research is an innovative not-for-profit research organisation at the forefront of developing tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics - with a strong focus on nuclear magnetic resonance (NMR) spectroscopy - as well as computational approaches and cellular studies. You will play a crucial role in shaping the future of this cutting-edge research initiative. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins. If you are dedicated to supporting the advancement science and technology in an innovative environment, we encourage you to apply! Role Overview We are seeking a Machine Learning Engineer to advance data-processing and model-building and deployment capabilities at Bind. This role includes developing new machine-learning models for highly complex and heterogeneous scientific data such as from nuclear magnetic resonance (NMR), deploying and productionizing these models internally and externally, contributing to open-source software, and large-scale data analysis, curation, and pipeline building. Key Responsibilities Develop innovative machine learning approaches to elucidate and quantify the interactions between small molecules and intrinsically disordered proteins Integrate molecular simulations and deep learning approaches using cutting-edge architectures Software Engineering Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching systems for internal and external use Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation Assist in optimising data collection practices in both computational and experimental teams Mentor and support Bind's interdisciplinary team in machine-learning and data analysis methods Driving Innovation Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies Contribute to the design and execution of cutting-edge machine learning and NMR research projects that advance Bind's scientific mission Thrive in a dynamic, start-up-style environment where initiative and flexibility in your role are valued. Qualifications and Expertise We encourage applications from software engineers, scientists, and individuals with relevant transferable skills who are enthusiastic about our mission to make disordered proteins druggable, even if they do not meet every requirement listed below. We believe innovation thrives through diverse perspectives and welcome candidates from both academic and industry backgrounds. Education and Experience MSc in a technical field with 3 years of machine learning or model-building experience or a PhD with a similar focus Extensive knowledge of machine learning approaches, neural network architectures, training methods, and data preparation best practices Experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series Strong dev-ops skillset, with proficiency in model deployment, versioning, distributed architectures, and containerization Track record of completed scientific software projects or open-source project contributions Skills and Abilities Strong written and verbal communication skills, with the ability to communicate effectively with team members in diverse fields Strong programming abilities in Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL Expertise with deep learning approaches such as diffusion or flow matching Proficiency in modern software development practices: code testing, documentation, packaging and deployment, version control using Git, containerization Proven ability to process, analyse, and present large and complex datasets using techniques such as clustering and dimensionality reduction Additional Attributes A collaborative mindset and an enthusiasm for interdisciplinary teamwork A strong engineering mindset: you believe ease-of-use, reproducibility, maintainability, and clear documentation are key requirements for scientific software and allow complex projects to gain results faster Dedication to continuous professional development in machine learning, dev-ops, programming, and a willingness to learn more about experimental biophysical methods Passion for contributing to the establishment and growth of a world-class not-for-profit research organisation Nice to Have Knowledge of NMR spectroscopy and associated data processing pipelines Familiarity with simulation techniques such as molecular dynamics or Monte Carlo approaches, as well as an understanding of statistical mechanics and complex systems Ability to use HPC and / or cloud computing and building automation and orchestration systems for these platforms Proficiency in a low-level language such as C, C++, or Rust and in GPU frameworks like CUDA Competence in front-end web design to allow easy interfacing with large datasets Our Culture Follow the science. We prioritise rigorous scientific inquiry, relying on evidence and expertise to guide decisions and actions, incorporating the latest research to achieve meaningful, ethical, and impactful outcomes for the public and scientific community. Think dynamically. We believe the most effective solutions come from a dynamic, adaptable mindset that embraces uncertainty as a catalyst for discovery, encouraging creativity, challenging assumptions, and approaching problems from multiple angles to foster innovation, navigate complexity, and deliver exceptional results. Celebrate a diverse ensemble. We celebrate diversity and inclusion, fostering a culture where all perspectives, backgrounds, and talents are valued, respected, and empowered to thrive, enabling us to better understand our community, collaborate effectively, and deliver impactful solutions. Build an innovation hub. We strive to advance disordered protein research by creating and sharing tools and datasets collaboratively, building on past contributions, and working alongside the disordered protein community to deepen understanding and maximise collective impact. What We Offer 38 days holiday (inclusive of bank holidays) Employer pension contribution in line with market standards Cycle to work scheme Life insurance Additional information The interview process will begin with a phone screen. Successful candidates will then be invited to more comprehensive technical and cultural interviews. To apply send your CV and cover letter to with the reference number BRJ017 and your name in the email header. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins! We are committed to protecting your personal information. For full details on how we collect, use, and store your data during the recruitment process, please refer to our Privacy Notice .
This isn't your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We're aiming to become Germany's second bootstrapped unicorn. Almedia is already Europe's fastest-growing company in 2025 (FT1000). We are building the future of marketing by rewarding our community of over 60 million users for engaging with our advertisers' products. We are offering a new way to acquire users for the biggest companies in the world. Machine Learning Engineer You'll take ownership of designing, developing, and scaling impactful, production-grade ML solutions that power Almedia's products and growth. This is a hands-on role involving active participation in code development and delivery. Types of problems you'll be solving: Designing and optimising user reward schemes based on player behaviour and market shifts Leading the design and implementation of solutions for personalised, real-time reward values Developing solutions for identifying underperforming reward campaigns and causes of failure Your role: Lead end-to-end delivery: build, deploy, and optimise solutions and services at scale Provide technical leadership across ML projects, ensuring best practices and high-quality code Align technical capabilities with business priorities to unlock high-value opportunities Apply advanced statistical and causal inference methods to ensure robustness and reliability Partner with product and engineering teams to translate business challenges into predictive, data-driven solutions You have: Proven expertise in building, deploying, and maintaining solutions and services in production, ideally in adtech or high scale environments Deep knowledge of statistics (A/B testing, regression, probability) Strong programming background in Python and SQL, with hands on cloud experience Ability to mentor and set technical direction for ML engineers and cross functional peers Strong communication skills to influence both technical and non technical stakeholders Bonus points for: Passion for gaming and strong understanding of player behaviour Experience with adtech, monetisation platforms, or the gambling industry Familiarity with gaming KPIs such as pLTV, retention, and ROAS Why Almedia? Own Our Growth: We offer all Berlin based employees equity in Almedia to truly be a part of our success. Scale With Almedia: Grow alongside a startup that has been profitable from day one. Central Berlin Office: Work from a fully stocked modern office built for collaboration, accessible from all around Berlin. Other Benefits: Transport subsidy, breakfasts and lunches, language learning, Urban Sports Club, and more. We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.
23/05/2026
Full time
This isn't your regular job. Almedia is a place where those who want to push harder can accelerate their careers faster than anywhere else. We're aiming to become Germany's second bootstrapped unicorn. Almedia is already Europe's fastest-growing company in 2025 (FT1000). We are building the future of marketing by rewarding our community of over 60 million users for engaging with our advertisers' products. We are offering a new way to acquire users for the biggest companies in the world. Machine Learning Engineer You'll take ownership of designing, developing, and scaling impactful, production-grade ML solutions that power Almedia's products and growth. This is a hands-on role involving active participation in code development and delivery. Types of problems you'll be solving: Designing and optimising user reward schemes based on player behaviour and market shifts Leading the design and implementation of solutions for personalised, real-time reward values Developing solutions for identifying underperforming reward campaigns and causes of failure Your role: Lead end-to-end delivery: build, deploy, and optimise solutions and services at scale Provide technical leadership across ML projects, ensuring best practices and high-quality code Align technical capabilities with business priorities to unlock high-value opportunities Apply advanced statistical and causal inference methods to ensure robustness and reliability Partner with product and engineering teams to translate business challenges into predictive, data-driven solutions You have: Proven expertise in building, deploying, and maintaining solutions and services in production, ideally in adtech or high scale environments Deep knowledge of statistics (A/B testing, regression, probability) Strong programming background in Python and SQL, with hands on cloud experience Ability to mentor and set technical direction for ML engineers and cross functional peers Strong communication skills to influence both technical and non technical stakeholders Bonus points for: Passion for gaming and strong understanding of player behaviour Experience with adtech, monetisation platforms, or the gambling industry Familiarity with gaming KPIs such as pLTV, retention, and ROAS Why Almedia? Own Our Growth: We offer all Berlin based employees equity in Almedia to truly be a part of our success. Scale With Almedia: Grow alongside a startup that has been profitable from day one. Central Berlin Office: Work from a fully stocked modern office built for collaboration, accessible from all around Berlin. Other Benefits: Transport subsidy, breakfasts and lunches, language learning, Urban Sports Club, and more. We believe in fostering talent, evaluating all skill levels during the hiring process, and providing a clear path for growth. Almedia is an equal opportunity employer. We embrace and celebrate diversity, and encourage individuals from all backgrounds to apply.
Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here. We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you'll be empowered to envision its most powerful applications, and to make them happen. About the Team Bringing medicine to patients is complex, expensive and high-risk. Faculty's Life Science's team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies. We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all. About the role Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems. What you'll be doing: Building and deploying production-grade ML software, tools, and infrastructure. Creating reusable, scalable solutions that accelerate the delivery of ML systems. Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges. Leading technical scoping and architectural decisions to ensure project feasibility and impact. Defining and implementing Faculty's standards for deploying machine learning at scale. Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders. Who we're looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch. You possess strong Python skills and solid experience in software engineering best practices. You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security. You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale. You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders. You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions. The Interview Process Talent Team Screen (30 minutes) Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes) Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. Some of our standout benefits: Unlimited Annual Leave Policy Private healthcare and dental Enhanced parental leave Family-Friendly Flexibility & Flexible working Sanctus Coaching Hybrid Working (2 days in our Old Street office, London) If you don't feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.
23/05/2026
Full time
Why Faculty? We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we've worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here. We don't chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence. Our business, and reputation, is growing fast and we're always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology. AI is an epoch-defining technology, join a company where you'll be empowered to envision its most powerful applications, and to make them happen. About the Team Bringing medicine to patients is complex, expensive and high-risk. Faculty's Life Science's team is concentrated on building AI solutions which optimise the research and commercialisation of life-changing therapies. We partner with major pharma firms, academic research centres and MedTech start-ups to design and deliver solutions which address critical healthcare challenges, and help to democratise health for all. About the role Join us as a Machine Learning Engineer to deliver bespoke, impactful AI solutions for our diverse clients. You will be instrumental in bringing machine learning out of the lab and into the real world, contributing to scalable software architecture and defining best practices. Working with clients, and cross-functional teams, you'll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems. What you'll be doing: Building and deploying production-grade ML software, tools, and infrastructure. Creating reusable, scalable solutions that accelerate the delivery of ML systems. Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges. Leading technical scoping and architectural decisions to ensure project feasibility and impact. Defining and implementing Faculty's standards for deploying machine learning at scale. Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders. Who we're looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch. You possess strong Python skills and solid experience in software engineering best practices. You bring hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP), including architecture and security. You've worked with container and orchestration tools such at Docker & Kubernetes to build and manage applications at scale. You are comfortable with core ML concepts, including probability, statistics, and common learning techniques. You're an excellent communicator, able to guide technical teams and confidently advise non-technical stakeholders. You thrive in a fast-paced environment, and enjoy the autonomy to own scope, solve and delivery solutions. The Interview Process Talent Team Screen (30 minutes) Pair Programming Interview (90 minutes) System Design Interview (90 minutes) Commercial Interview (60 minutes) Our Recruitment Ethos We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We're united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations. Some of our standout benefits: Unlimited Annual Leave Policy Private healthcare and dental Enhanced parental leave Family-Friendly Flexibility & Flexible working Sanctus Coaching Hybrid Working (2 days in our Old Street office, London) If you don't feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.
TL;DR Tilt is at the forefront of commerce, building a net-new way to buy and sell online. As we enter our next phase of growth, we're rebuilding the CEO Office and seeking a Machine Learning Engineer to work on personalisation at Tilt. This is a rare opportunity for someone to take our recommendation systems from 1 to 100. You will work on building real-time algorithms for high conversion across a video-first shopping platform that sits in its own class. You must be comfortable building feature pipelines, training models, deploying, and monitoring. About Tilt Tilt's mission is simple: Make Commerce Alive. From static store website builders to impersonal marketplaces, today's ecosystem is aging fast. It was built for transactional experiences, not for the new generation of merchants who grow through attention, community and personality. In the UK alone, millions of shoppers, from sneakerheads and Y2K girlies to collectors and parents, have signed up to Tilt. Our platform has helped sellers go from zero to £1M+ in earnings, and hundreds more earn above the UK median income. And we are just getting started. Your Mission You will own and build solutions for personalisation across different surfaces in our app, including live streams, products, videos, and more. Learn about our users, explore the data and engineer powerful signals Deliver ML algorithms which significantly outperform our current heuristic models Make Tilt the world leader in real-time recommendations & agentic personalisation As an AI/ML engineer, you will work closely with our Platform and Growth teams. There will be close collaboration with product, engineering and the business team. To be successful, you must be comfortable tackling a wide range of technical challenges. Location: Hybrid (3 days a week from London, King's Cross office - mandatory days Tuesdays & Thursdays plus one day of your choice!) What You'll Do 0 to 3 months Dive deep into the product and data to learn what the best personalisation experience is for customers and our business Rapidly onboard onto the existing recommendations system, identifying key improvements and implementing them Transition us from a scoring model to a more robust ML model Integrate novel new features and real time signals into the recommendation engine 3+ months Continue to ensure we are serving best-in-class recommendations for our customers Extend the recommendation system to more experiences in our app Integrate into our search experience, working with the broader team to invent a new type of search Who You Are An ML and AI enthusiast, you've built recommendation & ranking systems in production, not just research prototypes A builder: You can code and think in systems A scientist: You have strong foundations in data, statistics and machine learning An excellent communicator: Written, verbal and visual communication are superhuman strengths; you must be exceptional at at least 2 of these Autonomous: You do not require close supervision; you spot problems and solve them Comfortable operating in ambiguity, pressure, and high expectation environments Nice to Have Experience in fast paced, high-autonomy startup environments, ideally Series B+ Exposure to venture capital, private equity, or fundraising processes Built automations or software applications before Prior experience working directly with founders or CEOs Why Tilt You'll be joining a mission driven team backed by world class investors (TechCrunch) You'll own meaningful systems from day one, with real scope and autonomy You'll work alongside curious, kind, and wickedly smart teammates You'll help redefine how millions of people shop online Curious what it's like to work at Tilt? Start here. Or just download the app on the UK App Store or UK Google Play and see for yourself. Perks & Benefits 29 days off, plus UK bank holidays Your birthday off, no questions asked Share options to become a true stakeholder in our success 3% pension contribution from Month 2 (auto-enrolment) Unlimited phone/video and in person therapy (phone therapy covers partner and dependants 16-18 years old) 24/7 phone GP, including private prescriptions (including partner and children) MacBook and tech budget to get you set up your way Gym membership Free Deliveroo if you're working late We welcome applicants from all backgrounds and experiences, and we're committed to fostering an inclusive, diverse workplace. If you don't meet every single requirement in the job description, please don't be put off from applying. We value potential and a willingness to learn over ticking every box - your unique perspective could be exactly what we're looking for. Let us know if you need any adjustments during the application process - we're happy to help.
23/05/2026
Full time
TL;DR Tilt is at the forefront of commerce, building a net-new way to buy and sell online. As we enter our next phase of growth, we're rebuilding the CEO Office and seeking a Machine Learning Engineer to work on personalisation at Tilt. This is a rare opportunity for someone to take our recommendation systems from 1 to 100. You will work on building real-time algorithms for high conversion across a video-first shopping platform that sits in its own class. You must be comfortable building feature pipelines, training models, deploying, and monitoring. About Tilt Tilt's mission is simple: Make Commerce Alive. From static store website builders to impersonal marketplaces, today's ecosystem is aging fast. It was built for transactional experiences, not for the new generation of merchants who grow through attention, community and personality. In the UK alone, millions of shoppers, from sneakerheads and Y2K girlies to collectors and parents, have signed up to Tilt. Our platform has helped sellers go from zero to £1M+ in earnings, and hundreds more earn above the UK median income. And we are just getting started. Your Mission You will own and build solutions for personalisation across different surfaces in our app, including live streams, products, videos, and more. Learn about our users, explore the data and engineer powerful signals Deliver ML algorithms which significantly outperform our current heuristic models Make Tilt the world leader in real-time recommendations & agentic personalisation As an AI/ML engineer, you will work closely with our Platform and Growth teams. There will be close collaboration with product, engineering and the business team. To be successful, you must be comfortable tackling a wide range of technical challenges. Location: Hybrid (3 days a week from London, King's Cross office - mandatory days Tuesdays & Thursdays plus one day of your choice!) What You'll Do 0 to 3 months Dive deep into the product and data to learn what the best personalisation experience is for customers and our business Rapidly onboard onto the existing recommendations system, identifying key improvements and implementing them Transition us from a scoring model to a more robust ML model Integrate novel new features and real time signals into the recommendation engine 3+ months Continue to ensure we are serving best-in-class recommendations for our customers Extend the recommendation system to more experiences in our app Integrate into our search experience, working with the broader team to invent a new type of search Who You Are An ML and AI enthusiast, you've built recommendation & ranking systems in production, not just research prototypes A builder: You can code and think in systems A scientist: You have strong foundations in data, statistics and machine learning An excellent communicator: Written, verbal and visual communication are superhuman strengths; you must be exceptional at at least 2 of these Autonomous: You do not require close supervision; you spot problems and solve them Comfortable operating in ambiguity, pressure, and high expectation environments Nice to Have Experience in fast paced, high-autonomy startup environments, ideally Series B+ Exposure to venture capital, private equity, or fundraising processes Built automations or software applications before Prior experience working directly with founders or CEOs Why Tilt You'll be joining a mission driven team backed by world class investors (TechCrunch) You'll own meaningful systems from day one, with real scope and autonomy You'll work alongside curious, kind, and wickedly smart teammates You'll help redefine how millions of people shop online Curious what it's like to work at Tilt? Start here. Or just download the app on the UK App Store or UK Google Play and see for yourself. Perks & Benefits 29 days off, plus UK bank holidays Your birthday off, no questions asked Share options to become a true stakeholder in our success 3% pension contribution from Month 2 (auto-enrolment) Unlimited phone/video and in person therapy (phone therapy covers partner and dependants 16-18 years old) 24/7 phone GP, including private prescriptions (including partner and children) MacBook and tech budget to get you set up your way Gym membership Free Deliveroo if you're working late We welcome applicants from all backgrounds and experiences, and we're committed to fostering an inclusive, diverse workplace. If you don't meet every single requirement in the job description, please don't be put off from applying. We value potential and a willingness to learn over ticking every box - your unique perspective could be exactly what we're looking for. Let us know if you need any adjustments during the application process - we're happy to help.
Machine Learning Engineer Location London Employment Type Full time Location Type Hybrid Department Analytics & Data Compensation Competitive (Financial Services) About TradingHub Founded in 2010, TradingHub delivers uniquely intelligent trade surveillance software to world leading financial institutions. Developed by market professionals, our solutions use sophisticated modelling techniques to detect single and cross-product market manipulation. With a team of over 150 experts worldwide, TradingHub combines global reach with deep markets expertise to help our customers mitigate financial, regulatory, and reputational risk. The Role We're looking for a Machine Learning Engineer to join our Analytics division and play an important role in enhancing our metrics offering. As our first dedicated ML hire, you'll be utilising an array of modern LLM and NLP techniques to analyse complex financial data and unlock new capabilities for our market-leading suite of trade surveillance products. This role will see you combine hands on model development and software engineering, and collaborate with a high-performing team of Quant Researchers and Developers as well as other cross-functional departments. Responsibilities Design, develop, and deploy machine learning models to enhance TradingHub's market surveillance and analytics platform Contribute to the development of advanced metrics used to analyse trader behaviour, order execution and potential market abuse scenarios Apply machine learning and statistical techniques to large-scale financial datasets, improving accuracy and reducing false positives Leverage LLM and NLP models to extract insights from unstructured data and integrate them into existing analytics workflows Collaborate closely with quantitative developers, data engineers, and product teams to productionise models into scalable, high-performance systems Requirements Confident programming skills in Python, with experience using modern ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Good understanding of core machine learning concepts such as linear regression, reinforcement learning and deep learning Industry experience using Large Language Models (LLMs) to deliver commercial value Experience building data pipelines and performing feature engineering on real-world datasets Strong problem-solving skills and attention to detail Good understanding of SQL and working with complex datasets Keen interest in financial markets e.g. pricing, trading, fixed income Benefits Life at TradingHub is a rewarding journey within a fast growing company that thrives on innovation and collaboration. By combining the best of technology and global markets, we're able to solve complex problems together and deliver meaningful results to our customers. Everybody has value to bring, and we welcome individuality as a key driving force behind our collective success. Rooted in everything that we do are our core values: Accountability, Ambition, Partnership and Trust. These values provide the foundation for a sustainable workplace culture that empowers you to grow, contribute, and become your best self. Employee Benefits: Annual discretionary performance bonus (permanent employees only) Hybrid working policy Office lunches twice a week Private medical insurance + dental cover Extended parental leave (up to 6 months of fully paid maternity leave) 25 days annual leave + bank holidays Enhanced company pension plan 5 days study leave towards professional qualifications Salary sacrifice schemes Death in service coverage Equal Opportunity Statement TradingHub is an equal opportunities employer. We do not discriminate based on race, religion, ethnic or national origins, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, socioeconomic background, responsibilities for dependants, physical or mental disability or other applicable legally protected characteristics. TradingHub selects candidates for interview based solely on their skills, experience and qualifications.
23/05/2026
Full time
Machine Learning Engineer Location London Employment Type Full time Location Type Hybrid Department Analytics & Data Compensation Competitive (Financial Services) About TradingHub Founded in 2010, TradingHub delivers uniquely intelligent trade surveillance software to world leading financial institutions. Developed by market professionals, our solutions use sophisticated modelling techniques to detect single and cross-product market manipulation. With a team of over 150 experts worldwide, TradingHub combines global reach with deep markets expertise to help our customers mitigate financial, regulatory, and reputational risk. The Role We're looking for a Machine Learning Engineer to join our Analytics division and play an important role in enhancing our metrics offering. As our first dedicated ML hire, you'll be utilising an array of modern LLM and NLP techniques to analyse complex financial data and unlock new capabilities for our market-leading suite of trade surveillance products. This role will see you combine hands on model development and software engineering, and collaborate with a high-performing team of Quant Researchers and Developers as well as other cross-functional departments. Responsibilities Design, develop, and deploy machine learning models to enhance TradingHub's market surveillance and analytics platform Contribute to the development of advanced metrics used to analyse trader behaviour, order execution and potential market abuse scenarios Apply machine learning and statistical techniques to large-scale financial datasets, improving accuracy and reducing false positives Leverage LLM and NLP models to extract insights from unstructured data and integrate them into existing analytics workflows Collaborate closely with quantitative developers, data engineers, and product teams to productionise models into scalable, high-performance systems Requirements Confident programming skills in Python, with experience using modern ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Good understanding of core machine learning concepts such as linear regression, reinforcement learning and deep learning Industry experience using Large Language Models (LLMs) to deliver commercial value Experience building data pipelines and performing feature engineering on real-world datasets Strong problem-solving skills and attention to detail Good understanding of SQL and working with complex datasets Keen interest in financial markets e.g. pricing, trading, fixed income Benefits Life at TradingHub is a rewarding journey within a fast growing company that thrives on innovation and collaboration. By combining the best of technology and global markets, we're able to solve complex problems together and deliver meaningful results to our customers. Everybody has value to bring, and we welcome individuality as a key driving force behind our collective success. Rooted in everything that we do are our core values: Accountability, Ambition, Partnership and Trust. These values provide the foundation for a sustainable workplace culture that empowers you to grow, contribute, and become your best self. Employee Benefits: Annual discretionary performance bonus (permanent employees only) Hybrid working policy Office lunches twice a week Private medical insurance + dental cover Extended parental leave (up to 6 months of fully paid maternity leave) 25 days annual leave + bank holidays Enhanced company pension plan 5 days study leave towards professional qualifications Salary sacrifice schemes Death in service coverage Equal Opportunity Statement TradingHub is an equal opportunities employer. We do not discriminate based on race, religion, ethnic or national origins, sex (including pregnancy, childbirth, reproductive health decisions, or related medical conditions), sexual orientation, gender identity, gender expression, age, socioeconomic background, responsibilities for dependants, physical or mental disability or other applicable legally protected characteristics. TradingHub selects candidates for interview based solely on their skills, experience and qualifications.
Summary Imagine what you could do here. At Apple, great new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! Are you passionate about music, movies, and the world of Artificial Intelligence and Machine Learning? So are we! Join our Human-Centered AI team for Apple Media Services. In this role, you'll represent the user perspective on new features, review and analyze data, and evaluate AI models powering everything from search and recommendations to other innovative features. Collaborate with Data Scientists, Researchers, and Engineers to drive improvements across our platforms. Description We are looking for a Machine Learning Engineer focused on Evaluation & Insights for the Human-Centered AI team. In this role, you will bridge the gap between human perception and algorithmic performance, helping evaluate and optimize Foundation Models and generative AI systems. You will architect robust evaluation frameworks, design scalable MLOps pipelines for model assessment, and translate qualitative failure modes into programmatic guardrails and training signals (e.g., SFT, RLHF/DPO). This role blends deep ML engineering expertise with strong analytical judgment to assess, interpret, and improve the behavior of advanced AI models. You will work cross-functionally with Software Engineering, Product, Research and Responsible AI teams at Apple to ensure that our AI experiences are reliable, safe, and aligned with human expectations. Responsibilities Lead Rigorous Model Evaluations: Architect and execute comprehensive evaluation suites for LLMs and multimodal models, identifying edge cases in multi-step reasoning, factuality, adversarial robustness, safety, and alignment. Advanced Scoring Frameworks: Develop deterministic, heuristic, and LLM-assisted evaluation frameworks (e.g., LLM-as-a-judge, reward modeling) to quantify human-perceived quality metrics (e.g., helpfulness, hallucination rates). Actionable Signal Extraction: Translate qualitative failure modes into quantifiable loss patterns, programmatic guardrails, and actionable data mixture adjustments for model training and inference. Improve Performance: Partner with engineering teams to refine model behavior, leveraging evaluation telemetry to inform prompt engineering, Retrieval Augmented Generation (RAG) strategies, and model fine tuning. Latent Pattern Recognition: Apply advanced ML techniques (e.g., embedding based clustering, representation learning, perturbation analysis) to systematically map error taxonomies and latent failure manifolds in model outputs. MLOps & Automation: Develop robust MLOps workflows to codify evaluation metrics, automate regression testing across model checkpoints, and integrate human centric assessments into ML CI/CD pipelines. Distributed Evaluation Pipelines: Architect scalable, distributed inference and processing pipelines (e.g., Ray, vLLM) for high throughput model evaluation, automated annotation, and output analysis at scale. Human Centric Metrics: Define quantitative evaluation frameworks that capture nuanced human factors, including trust calibration, conversational state tracking, and interpretability. Auto Evaluator Systems: Build automated evaluation pipelines utilizing LLMs to assess outputs at scale, optimizing for high correlation with human baseline annotations. Cross Functional Partnership: Collaborate with ML researchers, software developers, and product managers across Apple to translate product requirements into scalable, reliable, and efficient model evaluation infrastructure. Minimum Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field, with relevant industry experience in ML Engineering or Applied Research. Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face). Strong ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives. Hands on experience developing, fine tuning, or evaluating LLMs, multimodal models, and NLP systems. Deep familiarity with AI quality metrics, hallucination detection techniques (e.g., SelfCheckGPT), model alignment (RLHF/DPO), and LLM as a judge frameworks (e.g., G Eval, DeepEval). Preferred Qualifications Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design. Proven experience building scalable ML inference pipelines, model evaluation workflows, and structured rating frameworks for large scale AI systems. Experience building internal tools or automated pipelines for ML workflows using tools like MLflow, Weights & Biases, or similar platforms. Strong familiarity with advanced prompt engineering, RAG architectures (vector databases, semantic search), and Fine Tuning. At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law.
22/05/2026
Full time
Summary Imagine what you could do here. At Apple, great new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish! Are you passionate about music, movies, and the world of Artificial Intelligence and Machine Learning? So are we! Join our Human-Centered AI team for Apple Media Services. In this role, you'll represent the user perspective on new features, review and analyze data, and evaluate AI models powering everything from search and recommendations to other innovative features. Collaborate with Data Scientists, Researchers, and Engineers to drive improvements across our platforms. Description We are looking for a Machine Learning Engineer focused on Evaluation & Insights for the Human-Centered AI team. In this role, you will bridge the gap between human perception and algorithmic performance, helping evaluate and optimize Foundation Models and generative AI systems. You will architect robust evaluation frameworks, design scalable MLOps pipelines for model assessment, and translate qualitative failure modes into programmatic guardrails and training signals (e.g., SFT, RLHF/DPO). This role blends deep ML engineering expertise with strong analytical judgment to assess, interpret, and improve the behavior of advanced AI models. You will work cross-functionally with Software Engineering, Product, Research and Responsible AI teams at Apple to ensure that our AI experiences are reliable, safe, and aligned with human expectations. Responsibilities Lead Rigorous Model Evaluations: Architect and execute comprehensive evaluation suites for LLMs and multimodal models, identifying edge cases in multi-step reasoning, factuality, adversarial robustness, safety, and alignment. Advanced Scoring Frameworks: Develop deterministic, heuristic, and LLM-assisted evaluation frameworks (e.g., LLM-as-a-judge, reward modeling) to quantify human-perceived quality metrics (e.g., helpfulness, hallucination rates). Actionable Signal Extraction: Translate qualitative failure modes into quantifiable loss patterns, programmatic guardrails, and actionable data mixture adjustments for model training and inference. Improve Performance: Partner with engineering teams to refine model behavior, leveraging evaluation telemetry to inform prompt engineering, Retrieval Augmented Generation (RAG) strategies, and model fine tuning. Latent Pattern Recognition: Apply advanced ML techniques (e.g., embedding based clustering, representation learning, perturbation analysis) to systematically map error taxonomies and latent failure manifolds in model outputs. MLOps & Automation: Develop robust MLOps workflows to codify evaluation metrics, automate regression testing across model checkpoints, and integrate human centric assessments into ML CI/CD pipelines. Distributed Evaluation Pipelines: Architect scalable, distributed inference and processing pipelines (e.g., Ray, vLLM) for high throughput model evaluation, automated annotation, and output analysis at scale. Human Centric Metrics: Define quantitative evaluation frameworks that capture nuanced human factors, including trust calibration, conversational state tracking, and interpretability. Auto Evaluator Systems: Build automated evaluation pipelines utilizing LLMs to assess outputs at scale, optimizing for high correlation with human baseline annotations. Cross Functional Partnership: Collaborate with ML researchers, software developers, and product managers across Apple to translate product requirements into scalable, reliable, and efficient model evaluation infrastructure. Minimum Qualifications Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Cognitive Science, or a related technical field, with relevant industry experience in ML Engineering or Applied Research. Advanced proficiency in Python and modern deep learning ecosystems (PyTorch, JAX, Hugging Face). Strong ability to interpret unstructured model outputs (text, transcripts, embedding spaces) and synthesize qualitative findings into actionable engineering guidance and training objectives. Hands on experience developing, fine tuning, or evaluating LLMs, multimodal models, and NLP systems. Deep familiarity with AI quality metrics, hallucination detection techniques (e.g., SelfCheckGPT), model alignment (RLHF/DPO), and LLM as a judge frameworks (e.g., G Eval, DeepEval). Preferred Qualifications Knowledge of human factors, HCI, or cognitive science methodologies as applied to AI system design. Proven experience building scalable ML inference pipelines, model evaluation workflows, and structured rating frameworks for large scale AI systems. Experience building internal tools or automated pipelines for ML workflows using tools like MLflow, Weights & Biases, or similar platforms. Strong familiarity with advanced prompt engineering, RAG architectures (vector databases, semantic search), and Fine Tuning. At Apple, we're not all the same. And that's our greatest strength. We draw on the differences in who we are, what we've experienced and how we think. Because to create products that serve everyone, we believe in including everyone. Therefore, we are committed to treating all applicants fairly and equally. As a registered Disability Confident employer, we will work with applicants to make any reasonable accommodations. Apple will consider for employment all qualified applicants with criminal backgrounds in a manner consistent with applicable law.
ROLE OVERVIEW: We are currently looking for a Machine Learning Engineer to join a leading research organisation based in London. As Machine Learning Engineer, you will be responsible for advancing data-processing, model-building, and deployment capabilities for a pioneering research organisation. KEY DUTIES AND RESPONSIBILITIES: Your duties as the Machine Learning Engineer will be varied; however, the key duties and responsibilities are as follows: 1. Develop deep-learning pipelines for nuclear magnetic resonance (NMR) data and innovative machine learning approaches to elucidate and quantify interactions between small molecules and intrinsically disordered proteins. 2. Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices, and build model-deployment and job-launching systems for internal and external use. 3. Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation, and mentoring the interdisciplinary team in machine-learning and data analysis methods. 4. Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies, contributing to the design and execution of cutting-edge machine learning and NMR research projects. ROLE REQUIREMENTS: To be successful in your application to this exciting role as Machine Learning Engineer, we are looking to identify the following on your profile and past history: 1. Relevant degree in a technical field with proven experience in machine learning or model-building. 2. Proven industry experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series. An ability to demonstrate innovative ways of working (for example work on disordered proteins and consideration of the next frontier in drug discovery) will be highly advantageous. 3. A working knowledge and practical experience with Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL. Key Words: Machine Learning Engineer / Data Processing / Model Building / NMR / Deep Learning / Computational Biophysics / Drug Discovery / Neural Networks / Scientific Software / Python / Nuclear Magnetic Resonance Hyper Recruitment Solutions Ltd (HRS) is an Equal Opportunities employer. We welcome applications from anyone who meets the role requirements. HRS exclusively supports the Life Science sectors, combining recruitment expertise with scientific knowledge to help you advance your career.
21/05/2026
Full time
ROLE OVERVIEW: We are currently looking for a Machine Learning Engineer to join a leading research organisation based in London. As Machine Learning Engineer, you will be responsible for advancing data-processing, model-building, and deployment capabilities for a pioneering research organisation. KEY DUTIES AND RESPONSIBILITIES: Your duties as the Machine Learning Engineer will be varied; however, the key duties and responsibilities are as follows: 1. Develop deep-learning pipelines for nuclear magnetic resonance (NMR) data and innovative machine learning approaches to elucidate and quantify interactions between small molecules and intrinsically disordered proteins. 2. Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices, and build model-deployment and job-launching systems for internal and external use. 3. Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation, and mentoring the interdisciplinary team in machine-learning and data analysis methods. 4. Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies, contributing to the design and execution of cutting-edge machine learning and NMR research projects. ROLE REQUIREMENTS: To be successful in your application to this exciting role as Machine Learning Engineer, we are looking to identify the following on your profile and past history: 1. Relevant degree in a technical field with proven experience in machine learning or model-building. 2. Proven industry experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series. An ability to demonstrate innovative ways of working (for example work on disordered proteins and consideration of the next frontier in drug discovery) will be highly advantageous. 3. A working knowledge and practical experience with Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL. Key Words: Machine Learning Engineer / Data Processing / Model Building / NMR / Deep Learning / Computational Biophysics / Drug Discovery / Neural Networks / Scientific Software / Python / Nuclear Magnetic Resonance Hyper Recruitment Solutions Ltd (HRS) is an Equal Opportunities employer. We welcome applications from anyone who meets the role requirements. HRS exclusively supports the Life Science sectors, combining recruitment expertise with scientific knowledge to help you advance your career.
Baringa is a global consulting firm that partners with leaders to drive change and create value. With deep industry expertise, and enabled by advanced technology, the firm helps clients to deliver with greater confidence and certainty. With over 2,000 people across the UK, Europe, North America, Asia and Australia, the firm combines global insight with local understanding. The firm works across energy and resources, financial services, government and public sector, consumer products and retail, pharmaceuticals and life sciences, manufacturing, and technology, media and telecoms, with capabilities spanning strategy, transformation and operational excellence - all powered by advanced technology, data, AI and digital innovation. Clients value Baringa's collaborative approach and the way its teams integrate seamlessly - all working with a shared understanding of what matters most. The firm is known for its kind, curious experts who listen closely and care deeply about client success as they help clients transform energy markets, modernise financial platforms, expand telecoms and digital networks through advanced data analytics, enable digital services in government, and unlock growth in consumer sectors. Certified as a Great Place to Work around the world, Baringa has been recognised by the Financial Times in 22 categories of its UK Leading Management Consultants rankings, and by Forbes for four consecutive years as one of the World's Best Management Consulting Firms. Our Solutions and AI Lab Team are looking for experienced Machine Learning Engineers to join the team. In SAIL, we build state-of-the-art AI solutions that help our clients with some of their biggest projects - ranging from tools that support energy networks forecast risk and adapt to climate change using empirically-derived resilience models, to image recognition software using satellite and aerial imagery, to genAI-powered applications including bespoke assistants and agents. We are focused on delivering value-adding solutions aligned to our client's specific needs. This expertise is applied across clients in all of our industry market sectors (Financial Services, Products & Services, Energy & Resources, Pharmaceutical & Lifesciences and Government). Curious what that impact looks like? Check out our ENA AI Platform case study to see how we accelerated low-carbon device roll-outs for the UK. What you will be doing Defining and implementing machine learning projects over the full lifecycle, from conception to data preparation, model engineering, evaluation and deployment and finally model monitoring and maintenance Establishing and developing ML Ops frameworks and standards for clients and embedding within their infrastructure Working with clients to take them on the journey, upskilling along the way and ensuring they are kept in the loop and can take ownership after you roll off the project Performing maturity assessments across clients' Cloud/AI environments and recommending improvements Building ML strategy blueprints and advising clients on the different technology options Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisations strategy, policies and standards and in some cases, help customers to define new policies, philosophies and standards Helping clients to identify risks and mitigations for their ML and DS programmes, as well as transition from on-prem to modern cloud-based infrastructures (AWS, Azure, GCP) Working with clients in key areas of ML model governance, such as in defining philosophies including fairness, transparency, interpretability, and accountability Your Skills and Experience We are seeking passionate and dynamic ML engineers who are excited by building production ML solutions, and keen to take an active part in the growth of the company. We're looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life. Passionate person who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements Advanced degree in computer science, mathematics, physics, engineering or related STEM field Strong problem-solving skills and solid grounding in classical ML and deep learning: from applied statistics and traditional machine learning algorithms to transformers and SOTA deep learning Excellent collaboration and communication skills, both with teams and in client-facing engagements Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning studio) Be a 'lifelong learner' and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you For UK & EU Your personal data will be retained by Baringa for up to two years, in accordance with our UK Recruitment Privacy Notice / EU Recruitment Privacy Notice , to evaluate your application and meet our legal and reporting obligations. In line with the General Data Protection Regulation (GDPR), you have the right to request access to, rectification, or erasure (subject to legal limitations) of your personal data. For more information, please contact us at For the USA Your personal data may be retained by Baringa for up to two years, as outlined in our Recruitment Privacy Notice (AMER & APAC) , to support the recruitment process and internal reporting requirements. Where applicable, and in accordance with relevant federal and state laws, you may have the right to request access or correction of your personal information. For further details, please contact Your personal data will be retained by Baringa for up to two years, in accordance with our Recruitment Privacy Notice (AMER & APAC) , to assess your application and meet applicable reporting and legal obligations. In line with the Australian Privacy Act and Singapore's Personal Data Protection Act (PDPA), you may have rights to access, correct, or request limited deletion of your personal data. For more information, please contact us at
20/05/2026
Full time
Baringa is a global consulting firm that partners with leaders to drive change and create value. With deep industry expertise, and enabled by advanced technology, the firm helps clients to deliver with greater confidence and certainty. With over 2,000 people across the UK, Europe, North America, Asia and Australia, the firm combines global insight with local understanding. The firm works across energy and resources, financial services, government and public sector, consumer products and retail, pharmaceuticals and life sciences, manufacturing, and technology, media and telecoms, with capabilities spanning strategy, transformation and operational excellence - all powered by advanced technology, data, AI and digital innovation. Clients value Baringa's collaborative approach and the way its teams integrate seamlessly - all working with a shared understanding of what matters most. The firm is known for its kind, curious experts who listen closely and care deeply about client success as they help clients transform energy markets, modernise financial platforms, expand telecoms and digital networks through advanced data analytics, enable digital services in government, and unlock growth in consumer sectors. Certified as a Great Place to Work around the world, Baringa has been recognised by the Financial Times in 22 categories of its UK Leading Management Consultants rankings, and by Forbes for four consecutive years as one of the World's Best Management Consulting Firms. Our Solutions and AI Lab Team are looking for experienced Machine Learning Engineers to join the team. In SAIL, we build state-of-the-art AI solutions that help our clients with some of their biggest projects - ranging from tools that support energy networks forecast risk and adapt to climate change using empirically-derived resilience models, to image recognition software using satellite and aerial imagery, to genAI-powered applications including bespoke assistants and agents. We are focused on delivering value-adding solutions aligned to our client's specific needs. This expertise is applied across clients in all of our industry market sectors (Financial Services, Products & Services, Energy & Resources, Pharmaceutical & Lifesciences and Government). Curious what that impact looks like? Check out our ENA AI Platform case study to see how we accelerated low-carbon device roll-outs for the UK. What you will be doing Defining and implementing machine learning projects over the full lifecycle, from conception to data preparation, model engineering, evaluation and deployment and finally model monitoring and maintenance Establishing and developing ML Ops frameworks and standards for clients and embedding within their infrastructure Working with clients to take them on the journey, upskilling along the way and ensuring they are kept in the loop and can take ownership after you roll off the project Performing maturity assessments across clients' Cloud/AI environments and recommending improvements Building ML strategy blueprints and advising clients on the different technology options Translating business requirements (both functional and non-functional) into solutions, ensuring compliance with the organisations strategy, policies and standards and in some cases, help customers to define new policies, philosophies and standards Helping clients to identify risks and mitigations for their ML and DS programmes, as well as transition from on-prem to modern cloud-based infrastructures (AWS, Azure, GCP) Working with clients in key areas of ML model governance, such as in defining philosophies including fairness, transparency, interpretability, and accountability Your Skills and Experience We are seeking passionate and dynamic ML engineers who are excited by building production ML solutions, and keen to take an active part in the growth of the company. We're looking for people who can both advise our clients and get hands on in technical delivery to bring a solution to life. Passionate person who is excited by problems within machine learning and can bring a good mix of technical delivery and core consulting skills in client engagements Advanced degree in computer science, mathematics, physics, engineering or related STEM field Strong problem-solving skills and solid grounding in classical ML and deep learning: from applied statistics and traditional machine learning algorithms to transformers and SOTA deep learning Excellent collaboration and communication skills, both with teams and in client-facing engagements Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries (e.g. Pytorch, Tensorflow) from early conceptualisation to full deployment in scalable production environments Ability to design, deploy and maintain ML solutions on modern frameworks to meet functional business requirements, adhering to software engineering best practices and with exposure to version control, testing, MLOps, CI/CD and API design Hands on experience in using one of 3 major cloud technologies (AWS, Azure or GCP) in a production environment, as well as ML platforms (e.g., AWS Sagemaker, Azure Machine Learning studio) Be a 'lifelong learner' and can demonstrate a drive to always be learning and developing your skillsets and develop the skillsets of others around you For UK & EU Your personal data will be retained by Baringa for up to two years, in accordance with our UK Recruitment Privacy Notice / EU Recruitment Privacy Notice , to evaluate your application and meet our legal and reporting obligations. In line with the General Data Protection Regulation (GDPR), you have the right to request access to, rectification, or erasure (subject to legal limitations) of your personal data. For more information, please contact us at For the USA Your personal data may be retained by Baringa for up to two years, as outlined in our Recruitment Privacy Notice (AMER & APAC) , to support the recruitment process and internal reporting requirements. Where applicable, and in accordance with relevant federal and state laws, you may have the right to request access or correction of your personal information. For further details, please contact Your personal data will be retained by Baringa for up to two years, in accordance with our Recruitment Privacy Notice (AMER & APAC) , to assess your application and meet applicable reporting and legal obligations. In line with the Australian Privacy Act and Singapore's Personal Data Protection Act (PDPA), you may have rights to access, correct, or request limited deletion of your personal data. For more information, please contact us at
Overview We'reKingfisher, A team made up of over 74,000 passionate people who bring Kingfisher - and all our other brands: B&Q, Screwfix, Brico Depot, Castorama andKoctasto life.Guided by our purposeBetter Homes. Better Lives. For Everyone.We believe a better world starts with better homes, and we work every day to make that a reality. Join us and help shape the future of home improvement. This is an opportunity to make a significant impact across one of the largest retail groups in Europe. We are looking for a Machine Learning Engineer who will support the delivery and operationalisation of advanced artificial intelligence solutions created by our Group AI team. Your work will help shape how millions of customers and colleagues experience our products, services and decision making across our retail brands. You will work as part of a high performing engineering team to build scalable machine learning systems, ensuring models are robust, efficient and suitable for a live environment. You will collaborate with engineering, product and architecture colleagues to improve tools, processes and practices that accelerate the use of artificial intelligence across the organisation. What's the job? Key Accountabilities / Responsibilities: Develop machine learning models and support their deployment into production Write production quality code that is robust, efficient and maintainable Contribute to the implementation and improvement of pipelines, tooling and automation Apply good engineering standards and practices in model development Monitor performance and contribute to ongoing optimisation of models Work with colleagues to understand requirements and priorities Share knowledge, contribute ideas and support a collaborative team culture What you'll bring Good understanding of computer science fundamentals, including data structures, algorithms and software design Practical experience with classical machine learning techniques and an awareness of modern approaches such as natural language processing and deep learning Strong Python skills and experience with common libraries such as Pandas, scikit-learn and Jupyter Experience working with SQL and data pipelines to prepare and transform data for model training Understanding of model evaluation, monitoring and improving performance in a production environment Familiarity with tools and practices for deploying models, ideally including Git, CI workflows and containerisation Comfortable working with statistical concepts to interpret data and assess model performance Ability to work collaboratively, communicate clearly and deliver work to agreed outcome How We Work We believe in flexibility and balance. Our hybrid model blends home working for focus with time spent connecting and collaborating - whether in our offices or at off site locations. On average within our Engineering team - 40% of your time involves in person collaboration. We value the perspectives new team members bring and encourage you to apply - even if you don't meet 100% of the requirements. What We Offer An inclusive environment where your potential is limited only by your imagination. We encourage new ideas, support experimentation, and strive to create a workplace where everyone can be their best self. We also offer a competitive benefits package and plenty of opportunities to stretch and grow your career. Diversity & Inclusion Our customers come from all walks of life - and so do we. We're committed to ensuring all colleagues, future colleagues, and applicants are treated equally, regardless of age, gender, marital or civil partnership status, ethnicity, culture, religion, belief, political opinion, disability, gender identity, gender expression, or sexual orientation.
20/05/2026
Full time
Overview We'reKingfisher, A team made up of over 74,000 passionate people who bring Kingfisher - and all our other brands: B&Q, Screwfix, Brico Depot, Castorama andKoctasto life.Guided by our purposeBetter Homes. Better Lives. For Everyone.We believe a better world starts with better homes, and we work every day to make that a reality. Join us and help shape the future of home improvement. This is an opportunity to make a significant impact across one of the largest retail groups in Europe. We are looking for a Machine Learning Engineer who will support the delivery and operationalisation of advanced artificial intelligence solutions created by our Group AI team. Your work will help shape how millions of customers and colleagues experience our products, services and decision making across our retail brands. You will work as part of a high performing engineering team to build scalable machine learning systems, ensuring models are robust, efficient and suitable for a live environment. You will collaborate with engineering, product and architecture colleagues to improve tools, processes and practices that accelerate the use of artificial intelligence across the organisation. What's the job? Key Accountabilities / Responsibilities: Develop machine learning models and support their deployment into production Write production quality code that is robust, efficient and maintainable Contribute to the implementation and improvement of pipelines, tooling and automation Apply good engineering standards and practices in model development Monitor performance and contribute to ongoing optimisation of models Work with colleagues to understand requirements and priorities Share knowledge, contribute ideas and support a collaborative team culture What you'll bring Good understanding of computer science fundamentals, including data structures, algorithms and software design Practical experience with classical machine learning techniques and an awareness of modern approaches such as natural language processing and deep learning Strong Python skills and experience with common libraries such as Pandas, scikit-learn and Jupyter Experience working with SQL and data pipelines to prepare and transform data for model training Understanding of model evaluation, monitoring and improving performance in a production environment Familiarity with tools and practices for deploying models, ideally including Git, CI workflows and containerisation Comfortable working with statistical concepts to interpret data and assess model performance Ability to work collaboratively, communicate clearly and deliver work to agreed outcome How We Work We believe in flexibility and balance. Our hybrid model blends home working for focus with time spent connecting and collaborating - whether in our offices or at off site locations. On average within our Engineering team - 40% of your time involves in person collaboration. We value the perspectives new team members bring and encourage you to apply - even if you don't meet 100% of the requirements. What We Offer An inclusive environment where your potential is limited only by your imagination. We encourage new ideas, support experimentation, and strive to create a workplace where everyone can be their best self. We also offer a competitive benefits package and plenty of opportunities to stretch and grow your career. Diversity & Inclusion Our customers come from all walks of life - and so do we. We're committed to ensuring all colleagues, future colleagues, and applicants are treated equally, regardless of age, gender, marital or civil partnership status, ethnicity, culture, religion, belief, political opinion, disability, gender identity, gender expression, or sexual orientation.
About us We are champions of rail, inspired to build a greener, more sustainable future of travel. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels. Machine learning and AI at Trainline Machine learning and AI are at the core of how Trainline is transforming travel, helping millions of customers make smarter, more sustainable journeys every day. Our ML models and AI solutions power critical aspects of our platform, including: Advanced search and recommendation capabilities across our mobile and web applications Pricing and routing optimisations to find the best fares for customers Personalised user experiences enhanced by agentic AI Data driven digital marketing systems AI agents improving customer support About the role We are looking for Machine Learning Engineers to join our team to shape the future of train travel. You'll be joining a high performing, deeply technical community of Machine Learning Engineers, Data Scientists, and Data Engineers to tackle complex problems by combining Trainline's rich datasets with cutting edge algorithms. What unites our team is an expertise in the field, a love of what we do, and the desire to create impactful solutions to support Trainline's goals of encouraging sustainable travel. As a part of Trainline you will be joining an environment where learning and development is top priority. You will have the opportunity to work with fellow ML & AI enthusiasts on large scale production systems, delivering highly impactful products that make a difference to our millions of customers. Key responsibilities Work in cross functional teams combining data scientists, software, data and machine learning engineers, and product managers Design and deliver machine learning models and/or AI solutions at scale that drive measurable impact for Trainline Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state of the art algorithms Create the tools, frameworks and libraries that enable the acceleration of our ML & AI product delivery and improve our workflows Take an active part in our AI and ML community and foster a culture of rigorous learning and experimentation What we're looking for Have an advanced degree in Computer Science, Mathematics, Statistics or a similar quantitative discipline Are proficient with Python, including open source data libraries (e.g., Pandas, Numpy, Scikit learn) Have experience productionising machine learning models and/or AI solutions Are an expert in one of predictive modelling, classification, regression, optimisation, NLP algorithms or recommendation systems Have experience with Spark Have knowledge of DevOps technologies such as Docker and Terraform and ML Ops practices and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have broad understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies Have great communication skills Nice to have Experience with transport industry and/or geographical information systems (GIS) Experience with cloud infrastructure Experience with large language models (fine tuning, retrieval augmented generation, agents) Experience with graph technology and/or algorithms Benefits and working conditions We offer private health and dental insurance, a generous work from abroad policy, 2 for 1 share purchase plans, an EV scheme, extra festive time off, and family friendly benefits. We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets and regular learning days. We operate a hybrid model, asking that Trainliners work from the office a minimum of 60 % of the time over a 12 week period, and we also have a 28 day work from abroad policy. Equal Opportunity Employer We know that having a diverse team makes us better and helps us succeed. We are committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated.
20/05/2026
Full time
About us We are champions of rail, inspired to build a greener, more sustainable future of travel. Trainline enables millions of travellers to find and book the best value tickets across carriers, fares, and journey options through our highly rated mobile app, website, and B2B partner channels. Machine learning and AI at Trainline Machine learning and AI are at the core of how Trainline is transforming travel, helping millions of customers make smarter, more sustainable journeys every day. Our ML models and AI solutions power critical aspects of our platform, including: Advanced search and recommendation capabilities across our mobile and web applications Pricing and routing optimisations to find the best fares for customers Personalised user experiences enhanced by agentic AI Data driven digital marketing systems AI agents improving customer support About the role We are looking for Machine Learning Engineers to join our team to shape the future of train travel. You'll be joining a high performing, deeply technical community of Machine Learning Engineers, Data Scientists, and Data Engineers to tackle complex problems by combining Trainline's rich datasets with cutting edge algorithms. What unites our team is an expertise in the field, a love of what we do, and the desire to create impactful solutions to support Trainline's goals of encouraging sustainable travel. As a part of Trainline you will be joining an environment where learning and development is top priority. You will have the opportunity to work with fellow ML & AI enthusiasts on large scale production systems, delivering highly impactful products that make a difference to our millions of customers. Key responsibilities Work in cross functional teams combining data scientists, software, data and machine learning engineers, and product managers Design and deliver machine learning models and/or AI solutions at scale that drive measurable impact for Trainline Own the full end to end machine learning delivery lifecycle including data exploration, feature engineering, model selection and tuning, offline and online evaluation, deployments and maintenance Partner with stakeholders to propose innovative data products that leverage Trainline's extensive datasets and state of the art algorithms Create the tools, frameworks and libraries that enable the acceleration of our ML & AI product delivery and improve our workflows Take an active part in our AI and ML community and foster a culture of rigorous learning and experimentation What we're looking for Have an advanced degree in Computer Science, Mathematics, Statistics or a similar quantitative discipline Are proficient with Python, including open source data libraries (e.g., Pandas, Numpy, Scikit learn) Have experience productionising machine learning models and/or AI solutions Are an expert in one of predictive modelling, classification, regression, optimisation, NLP algorithms or recommendation systems Have experience with Spark Have knowledge of DevOps technologies such as Docker and Terraform and ML Ops practices and platforms like ML Flow Have experience with agile delivery methodologies and CI/CD processes and tools Have broad understanding of data extraction, data manipulation and feature engineering techniques Are familiar with statistical methodologies Have great communication skills Nice to have Experience with transport industry and/or geographical information systems (GIS) Experience with cloud infrastructure Experience with large language models (fine tuning, retrieval augmented generation, agents) Experience with graph technology and/or algorithms Benefits and working conditions We offer private health and dental insurance, a generous work from abroad policy, 2 for 1 share purchase plans, an EV scheme, extra festive time off, and family friendly benefits. We prioritise career growth with clear career paths, transparent pay bands, personal learning budgets and regular learning days. We operate a hybrid model, asking that Trainliners work from the office a minimum of 60 % of the time over a 12 week period, and we also have a 28 day work from abroad policy. Equal Opportunity Employer We know that having a diverse team makes us better and helps us succeed. We are committed to creating inclusive places to work, where everyone belongs and differences are valued and celebrated.
Machine Learning Engineer £700-750/day overall assignment rate to umbrella Fully remote 6 month initial Apply today to join a forward-thinking, tech-driven FTSE 100 organisation using data science and AI to enhance customer experience, optimise supply chains and drive sustainable growth. With 40% of sales from sustainable products, this is a company that combines scale, innovation and purpose. As a Machine Learning Engineer, you'll help maintain the stability and performance of core data and ML systems across Europe. This technical engineering role focuses on reliability, optimisation and critical fixes, ideal if you excel at investigating and debugging complex data flows and ML issues in live production environments. We're looking for individuals with: Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built, and we welcome applications from people of all backgrounds and perspectives. Our success is driven by our people, united by the spirit of partnership to deliver the best resourcing solutions for our clients. If you need any help or adjustments during the recruitment process for any reason , please let us know when you apply or talk to the recruiters directly so we can support you.
19/05/2026
Contractor
Machine Learning Engineer £700-750/day overall assignment rate to umbrella Fully remote 6 month initial Apply today to join a forward-thinking, tech-driven FTSE 100 organisation using data science and AI to enhance customer experience, optimise supply chains and drive sustainable growth. With 40% of sales from sustainable products, this is a company that combines scale, innovation and purpose. As a Machine Learning Engineer, you'll help maintain the stability and performance of core data and ML systems across Europe. This technical engineering role focuses on reliability, optimisation and critical fixes, ideal if you excel at investigating and debugging complex data flows and ML issues in live production environments. We're looking for individuals with: Experience: Proven background as a Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built, and we welcome applications from people of all backgrounds and perspectives. Our success is driven by our people, united by the spirit of partnership to deliver the best resourcing solutions for our clients. If you need any help or adjustments during the recruitment process for any reason , please let us know when you apply or talk to the recruiters directly so we can support you.
Location London Address London, England Employment Type Full time Location Type Hybrid Department Machine Learning Our Mission At Carbon Re We're on a mission to cut gigatonnes of carbon emissions from the world's biggest emitting industries, like cement, steel, and glass, by applying cutting edge AI where it matters most. We're a small and growing team of scientists, engineers, and strategic thinkers who care deeply about impact and believe in getting there with good humour and urgency. Our SaaS products help heavy industry optimise operations in real time, cutting costs and carbon today while building the foundation for the next industrial revolution. We are seeking a Senior Machine Learning Engineer to help build the models that underpin these control systems and help us level up our machine learning infrastructure. We don't draw a specific line between engineering and research teams. We operate as one cohesive unit, sharing tech stack, knowledge, and objectives. Our focus spans from fundamental ML research to commercial grade software development, offering diverse learning and impact opportunities. Your main responsibilities Work in the machine learning team as an individual contributor, building, testing and deploying our models. Contribute to technical innovation and problem solving across the machine learning lifecycle. Collaborate with the product team on customer projects, planning, designing and delivering the work packages required, as well as playing a significant role in the development of our wider product. Help establish best practices to improve our internal processes. Contribute to the design and implementation of robust, maintainable and scalable machine learning systems. You will also contribute to our fear free development process by building tooling that helps the team move faster and more sustainably. You will be supported by continuous builds, tests, a constructive review system, and a strong culture of improving engineering processes. What a great fit looks like You have 2 or more years of experience as a machine learning engineer. You are familiar with several ML techniques, and have both theoretical ML knowledge and experience implementing different types of solutions. You are proficient in Python and have a good understanding of the ecosystem of tools and libraries that support ML development (e.g., scikit learn, PyTorch). You have experience working in a scientific environment across disciplines (particularly physics, chemistry, materials science, and engineering), either through previous roles or study. Are passionate about making a positive impact on climate change mitigation and possess a strong interest in our mission. You'll excel if You have prior experience with time series modelling and industrial or IoT data. You have experience in any of: dynamical systems, reinforcement learning, system identification, optimisation or Bayesian statistics. You are used to working in a fast paced startup environment with an agile process. You have a degree in machine learning, physics or chemistry. You are hungry for responsibility, enthusiastic about taking on the design and development of solutions to difficult problems, and eager to drive the progress of new products. You have a solid understanding of modern cloud compute infrastructure as it relates to machine learning, and experience in working with AWS, GCP, Azure, or other vendors. The interview process We run a multiple part interview process. You can choose to interview remotely or on site for some of the interviews, but it's easier to build rapport in person. Intro call - meeting with our talent partner Fundamentals of Machine Learning - a discussion with members of the machine learning team around some of the fundamentals of ML and your understanding and application of them. (1 hour, remote) Technical interview - (half day, in person/remote) Problem solving - applying machine learning, scientific understanding and problem solving to some of the challenges we tackle day to day in the ML team. Engineering - a practical exercise focused on software engineering for ML. Architecture - a discussion based exercise around systems design for ML. Behaviours and Operating Principles - a meeting with two members of our team to discuss your past experiences, to understand how you would fit in with our operating principles. (1 hour, remote) Meet the exec - an informal chat to meet either Josh (CEO) or Buffy (COO) (30 minutes, in person/remote) In the same way we reference check our candidates before making final offers, we invite you to reference check us by chatting informally with any team members you didn't meet during the hiring process. Once the interviews are over, we'll try to make a decision as quickly as possible, and you can ask us for feedback at any stage. In return for your hard work, we'll give you Equity in the company: When we win, you win. You'll get share options, so you're part of our journey from the inside. Flexible working: We trust you to know how and when you work best and to work that out with your team. 30 days of holiday (plus bank holidays). Rest is productive. Take the time you need to recharge. A generous pension scheme: We're planning for the future in more ways than one. Our Operating Principles Go Gig or Go Home: High Bar, All In. What we do matters to humanity, to our customers and to each other. We hold ourselves to an extraordinarily high bar and bring the urgency this mission requires. Concrete Honesty: Be honest. As concrete forms the foundation of our world, genuine honesty and transparency are the bedrock of our culture. Autonomous Ownership: High agency, high ownership. We build systems that take control and make things better. We do the same: see it, own it, drive it. Cement it with Kindness & Fun: Have fun, be kind. We're here to extend Earth's life, but ours is still limited. We want to enjoy the ride.
18/05/2026
Full time
Location London Address London, England Employment Type Full time Location Type Hybrid Department Machine Learning Our Mission At Carbon Re We're on a mission to cut gigatonnes of carbon emissions from the world's biggest emitting industries, like cement, steel, and glass, by applying cutting edge AI where it matters most. We're a small and growing team of scientists, engineers, and strategic thinkers who care deeply about impact and believe in getting there with good humour and urgency. Our SaaS products help heavy industry optimise operations in real time, cutting costs and carbon today while building the foundation for the next industrial revolution. We are seeking a Senior Machine Learning Engineer to help build the models that underpin these control systems and help us level up our machine learning infrastructure. We don't draw a specific line between engineering and research teams. We operate as one cohesive unit, sharing tech stack, knowledge, and objectives. Our focus spans from fundamental ML research to commercial grade software development, offering diverse learning and impact opportunities. Your main responsibilities Work in the machine learning team as an individual contributor, building, testing and deploying our models. Contribute to technical innovation and problem solving across the machine learning lifecycle. Collaborate with the product team on customer projects, planning, designing and delivering the work packages required, as well as playing a significant role in the development of our wider product. Help establish best practices to improve our internal processes. Contribute to the design and implementation of robust, maintainable and scalable machine learning systems. You will also contribute to our fear free development process by building tooling that helps the team move faster and more sustainably. You will be supported by continuous builds, tests, a constructive review system, and a strong culture of improving engineering processes. What a great fit looks like You have 2 or more years of experience as a machine learning engineer. You are familiar with several ML techniques, and have both theoretical ML knowledge and experience implementing different types of solutions. You are proficient in Python and have a good understanding of the ecosystem of tools and libraries that support ML development (e.g., scikit learn, PyTorch). You have experience working in a scientific environment across disciplines (particularly physics, chemistry, materials science, and engineering), either through previous roles or study. Are passionate about making a positive impact on climate change mitigation and possess a strong interest in our mission. You'll excel if You have prior experience with time series modelling and industrial or IoT data. You have experience in any of: dynamical systems, reinforcement learning, system identification, optimisation or Bayesian statistics. You are used to working in a fast paced startup environment with an agile process. You have a degree in machine learning, physics or chemistry. You are hungry for responsibility, enthusiastic about taking on the design and development of solutions to difficult problems, and eager to drive the progress of new products. You have a solid understanding of modern cloud compute infrastructure as it relates to machine learning, and experience in working with AWS, GCP, Azure, or other vendors. The interview process We run a multiple part interview process. You can choose to interview remotely or on site for some of the interviews, but it's easier to build rapport in person. Intro call - meeting with our talent partner Fundamentals of Machine Learning - a discussion with members of the machine learning team around some of the fundamentals of ML and your understanding and application of them. (1 hour, remote) Technical interview - (half day, in person/remote) Problem solving - applying machine learning, scientific understanding and problem solving to some of the challenges we tackle day to day in the ML team. Engineering - a practical exercise focused on software engineering for ML. Architecture - a discussion based exercise around systems design for ML. Behaviours and Operating Principles - a meeting with two members of our team to discuss your past experiences, to understand how you would fit in with our operating principles. (1 hour, remote) Meet the exec - an informal chat to meet either Josh (CEO) or Buffy (COO) (30 minutes, in person/remote) In the same way we reference check our candidates before making final offers, we invite you to reference check us by chatting informally with any team members you didn't meet during the hiring process. Once the interviews are over, we'll try to make a decision as quickly as possible, and you can ask us for feedback at any stage. In return for your hard work, we'll give you Equity in the company: When we win, you win. You'll get share options, so you're part of our journey from the inside. Flexible working: We trust you to know how and when you work best and to work that out with your team. 30 days of holiday (plus bank holidays). Rest is productive. Take the time you need to recharge. A generous pension scheme: We're planning for the future in more ways than one. Our Operating Principles Go Gig or Go Home: High Bar, All In. What we do matters to humanity, to our customers and to each other. We hold ourselves to an extraordinarily high bar and bring the urgency this mission requires. Concrete Honesty: Be honest. As concrete forms the foundation of our world, genuine honesty and transparency are the bedrock of our culture. Autonomous Ownership: High agency, high ownership. We build systems that take control and make things better. We do the same: see it, own it, drive it. Cement it with Kindness & Fun: Have fun, be kind. We're here to extend Earth's life, but ours is still limited. We want to enjoy the ride.
ONYX Insight is seeking an ML Engineer to join our Advanced Analytics team on a 6 month contract, focusing on building and deploying production grade machine learning solutions for the renewable energy sector. The Role This is a delivery focused contract role, ideal for an ML Engineer who enjoys turning models into scalable, high quality software. You'll work closely with Data Scientists and Software Engineers to productionise AI models used in wind turbine condition monitoring and predictive maintenance. Key Responsibilities Design, build and maintain production ML services and APIs Implement model inference pipelines and optimise performance Develop robust data and feature pipelines integrating cloud data sources Write clean, testable, well documented code following modern software engineering practices Containerise and deploy ML solutions using Docker and CI/CD pipelines Support model deployment, monitoring and performance evaluation in live environments About You 3+ years' experience in ML Engineering or applied ML Strong commercial experience with Python and C# /.NET Experience deploying ML models into production environments Familiar with cloud platforms (AWS and/or Databricks preferred) Confident working end to end - from prototype to live system Background experience in industrial analytics, renewable energy, or condition monitoring is advantageous but not required. About ONYX ONYX Insight is a growing technology and engineering organisation in the renewable energy sector. Our vision is to build a more efficient future by becoming the world's most innovative provider of predictive technology solutions. Our advanced sensing, software and analytics combined with our engineering experience are deployed on wind turbines around the world to maximise production and make turbines more reliable for longer, optimising energy production. ONYX Insight is part of the Macquarie Group. Macquarie is a global financial services group operating in 34 markets in asset management, leasing and asset financing, market access, commodity trading, renewables development, specialist advisory services, capital raising and principal investment. The diversity of the Macquarie Group operations combined with a strong capital position and robust risk management framework has contributed to a 54 year-record of unbroken profitability. For any further information, or to understand our products and services better, please feel free to look through our website: ONYX Insight are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Recruitment Agencies We prioritise sourcing candidates directly wherever possible. For specific roles, we may engage preferred suppliers, invited by our Talent Acquisition Team, to support the process. CVs from other suppliers may be considered on an ad hoc basis, subject to prior written agreement. We will not be liable for fees related to CVs or profiles submitted directly to ONYX Insight employees outside of the agreed resourcing process. Agencies must liaise with our Talent Acquisition Team before submitting any unsolicited applications.
18/05/2026
Full time
ONYX Insight is seeking an ML Engineer to join our Advanced Analytics team on a 6 month contract, focusing on building and deploying production grade machine learning solutions for the renewable energy sector. The Role This is a delivery focused contract role, ideal for an ML Engineer who enjoys turning models into scalable, high quality software. You'll work closely with Data Scientists and Software Engineers to productionise AI models used in wind turbine condition monitoring and predictive maintenance. Key Responsibilities Design, build and maintain production ML services and APIs Implement model inference pipelines and optimise performance Develop robust data and feature pipelines integrating cloud data sources Write clean, testable, well documented code following modern software engineering practices Containerise and deploy ML solutions using Docker and CI/CD pipelines Support model deployment, monitoring and performance evaluation in live environments About You 3+ years' experience in ML Engineering or applied ML Strong commercial experience with Python and C# /.NET Experience deploying ML models into production environments Familiar with cloud platforms (AWS and/or Databricks preferred) Confident working end to end - from prototype to live system Background experience in industrial analytics, renewable energy, or condition monitoring is advantageous but not required. About ONYX ONYX Insight is a growing technology and engineering organisation in the renewable energy sector. Our vision is to build a more efficient future by becoming the world's most innovative provider of predictive technology solutions. Our advanced sensing, software and analytics combined with our engineering experience are deployed on wind turbines around the world to maximise production and make turbines more reliable for longer, optimising energy production. ONYX Insight is part of the Macquarie Group. Macquarie is a global financial services group operating in 34 markets in asset management, leasing and asset financing, market access, commodity trading, renewables development, specialist advisory services, capital raising and principal investment. The diversity of the Macquarie Group operations combined with a strong capital position and robust risk management framework has contributed to a 54 year-record of unbroken profitability. For any further information, or to understand our products and services better, please feel free to look through our website: ONYX Insight are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Recruitment Agencies We prioritise sourcing candidates directly wherever possible. For specific roles, we may engage preferred suppliers, invited by our Talent Acquisition Team, to support the process. CVs from other suppliers may be considered on an ad hoc basis, subject to prior written agreement. We will not be liable for fees related to CVs or profiles submitted directly to ONYX Insight employees outside of the agreed resourcing process. Agencies must liaise with our Talent Acquisition Team before submitting any unsolicited applications.
About Us We are a VC backed startup focused on hyper personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision making models to build the most engaging customer experiences for in store retail. Our mission is to change retail forever through hyper personalised experiences that are both simple and beautiful. About the Role We are looking for a Machine Learning Engineer with strong software engineering fundamentals to join our team of domain experts and researchers. You will be responsible for building robust, scalable ML systems that bring our foundation models for retail from prototype to production. Key Responsibilities Design and build production grade ML infrastructure, including training pipelines, model serving, and monitoring systems. Collaborate with research engineers to translate experimental models into reliable, maintainable software. Optimise ML systems for performance, scalability, and cost efficiency in cloud environments (distributed clusters, GPUs). Establish engineering best practices for ML development, including testing, CI/CD, and code review standards. Progression Timeline Month 1: Onboard to existing ML codebase and infrastructure; identify technical debt and reliability gaps; ship incremental improvements to model serving latency or pipeline robustness. Month 3: Own and deliver a major infrastructure component (e.g., feature store, training orchestration, or model registry); improve system observability with logging, metrics, and alerting. Month 6: Lead the end to end productionisation of our foundation model, meeting latency, throughput, and reliability SLAs; mentor teammates on engineering standards and contribute to architectural decisions. Essential Qualifications 3-5+ years building and maintaining ML systems in production environments. BSc or MSc in Computer Science, Software Engineering, or a related field. Strong software engineering skills: clean code, testing, debugging, version control, and system design. Proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, or JAX). Hands on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes). Solid understanding of ML fundamentals (model training, evaluation, common architectures). Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar). Building data pipelines (real time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt. Familiarity with recommender systems, transformers, or graph neural networks. Exposure to model optimisation techniques (quantisation, distillation, efficient inference). What We Offer Opportunity to build technology that will transform millions of shopping experiences. Real ownership and impact in shaping product and company direction. A dynamic, collaborative work environment with cutting edge ML challenges. Competitive compensation and equity in a rapidly growing company. If you're excited by the idea of shaping the future of retail and eager to make a real impact from day one, we'd love to hear from you.
17/05/2026
Full time
About Us We are a VC backed startup focused on hyper personalisation, currently in stealth. Inspired by the latest in recommender systems, we leverage transformers and graph learning alongside decision making models to build the most engaging customer experiences for in store retail. Our mission is to change retail forever through hyper personalised experiences that are both simple and beautiful. About the Role We are looking for a Machine Learning Engineer with strong software engineering fundamentals to join our team of domain experts and researchers. You will be responsible for building robust, scalable ML systems that bring our foundation models for retail from prototype to production. Key Responsibilities Design and build production grade ML infrastructure, including training pipelines, model serving, and monitoring systems. Collaborate with research engineers to translate experimental models into reliable, maintainable software. Optimise ML systems for performance, scalability, and cost efficiency in cloud environments (distributed clusters, GPUs). Establish engineering best practices for ML development, including testing, CI/CD, and code review standards. Progression Timeline Month 1: Onboard to existing ML codebase and infrastructure; identify technical debt and reliability gaps; ship incremental improvements to model serving latency or pipeline robustness. Month 3: Own and deliver a major infrastructure component (e.g., feature store, training orchestration, or model registry); improve system observability with logging, metrics, and alerting. Month 6: Lead the end to end productionisation of our foundation model, meeting latency, throughput, and reliability SLAs; mentor teammates on engineering standards and contribute to architectural decisions. Essential Qualifications 3-5+ years building and maintaining ML systems in production environments. BSc or MSc in Computer Science, Software Engineering, or a related field. Strong software engineering skills: clean code, testing, debugging, version control, and system design. Proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, or JAX). Hands on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes). Solid understanding of ML fundamentals (model training, evaluation, common architectures). Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar). Building data pipelines (real time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt. Familiarity with recommender systems, transformers, or graph neural networks. Exposure to model optimisation techniques (quantisation, distillation, efficient inference). What We Offer Opportunity to build technology that will transform millions of shopping experiences. Real ownership and impact in shaping product and company direction. A dynamic, collaborative work environment with cutting edge ML challenges. Competitive compensation and equity in a rapidly growing company. If you're excited by the idea of shaping the future of retail and eager to make a real impact from day one, we'd love to hear from you.
Within Tesco Data & Analytics, we help our customers and the communities where we operate get the most value from data. We build and run Tesco's data platforms, we architect and engineer data onto these platforms, provide capabilities and tools to the analytics community across Tesco, and develop data products at scale. Our Data Science teams are involved in a broad range of projects, spanning across supply chain, logistics, store and online. These include projects in the areas of Operations Optimisations, Commercial Decision Support (e.g. Forecasting and Range Optimisation), Online (e.g. Search and Recommendation) and Intelligent Edge (e.g. Computer Vision). Our Machine Learning Engineers work alongside our data scientists, helping with everything from development of tools and platforms, code optimisations through to deployment of solutions on the edge, cloud and big data environments. As a Machine Learning Engineer, you'll be a significant contributor to the delivery of products in one of Tesco's most strategic technology areas. You'll work with other engineers, data scientists, product managers, systems engineers, and analytics professionals to help deliver valuable and innovative outcomes for our customers. You'll work within and across our Engineering and Data Science teams, delivering scalable products that improve how we serve our customers and run our operations. Responsibilities Participating in group discussions on system design and architecture. Working with product teams to communicate and translate needs into technical requirements. Working with Data Scientists, Engineers and Product teams across the software lifecycle. Delivering high quality code and solutions, bringing solutions into production. Performing code reviews to optimise technical performance of data science solutions. Supporting production systems, resolving incidents, and performing root cause analysis. Continually looking for how we can evolve and improve our technology, processes and practices. Sharing knowledge with the wider engineering community. Applying SDLC practices to create and release robust software. You'll come from either an Engineering or Data Science background, with a good understanding of the Data Science Toolkit (Programming, Machine Learning, MLOps etc) and bringing data science solutions into production. You therefore tick the majority of the following points: Key Requirements A higher degree in engineering, computer science, maths or science. Customer focus with the right balance between outcome delivery and technical excellence. The ability to apply technical skills and know how to solving real world business problems. Demonstratable experience of building scalable and resilient systems. Commercial experience contributing to the success of high impact Data Science projects within complex organisations. Awareness of emerging MLOps practices and tooling would be an advantage e.g. feature stores and model lifecycle management. An analytical mind set and the ability to tackle specific business problems. Experience with different programming languages and a good grasp of at least one language. The ideal candidate is fluent in Python. Use of version control (Git) and related software lifecycle tooling. Experience with tooling for monitoring, logging and alerting e.g. Splunk or Grafana. Understanding of common data structures and algorithms. Experience working with open-source Data Science environments. Knowledge of open source big data technologies such as Apache Spark. Experience building solutions that run in the cloud, ideally Azure. Experience with software development methodologies including Scrum & Kanban. Working patterns We're a big business and we can offer a range of diverse full time & part time working patterns across our many business areas, which means that we can find something that works for you. We work in a more blended pattern - combining office and remote working. Our offices will continue to be where we connect, collaborate and innovate. If you are applying internally, please speak to the Hiring Manager about how this can work for you - Everyone is welcome at Tesco.
16/05/2026
Full time
Within Tesco Data & Analytics, we help our customers and the communities where we operate get the most value from data. We build and run Tesco's data platforms, we architect and engineer data onto these platforms, provide capabilities and tools to the analytics community across Tesco, and develop data products at scale. Our Data Science teams are involved in a broad range of projects, spanning across supply chain, logistics, store and online. These include projects in the areas of Operations Optimisations, Commercial Decision Support (e.g. Forecasting and Range Optimisation), Online (e.g. Search and Recommendation) and Intelligent Edge (e.g. Computer Vision). Our Machine Learning Engineers work alongside our data scientists, helping with everything from development of tools and platforms, code optimisations through to deployment of solutions on the edge, cloud and big data environments. As a Machine Learning Engineer, you'll be a significant contributor to the delivery of products in one of Tesco's most strategic technology areas. You'll work with other engineers, data scientists, product managers, systems engineers, and analytics professionals to help deliver valuable and innovative outcomes for our customers. You'll work within and across our Engineering and Data Science teams, delivering scalable products that improve how we serve our customers and run our operations. Responsibilities Participating in group discussions on system design and architecture. Working with product teams to communicate and translate needs into technical requirements. Working with Data Scientists, Engineers and Product teams across the software lifecycle. Delivering high quality code and solutions, bringing solutions into production. Performing code reviews to optimise technical performance of data science solutions. Supporting production systems, resolving incidents, and performing root cause analysis. Continually looking for how we can evolve and improve our technology, processes and practices. Sharing knowledge with the wider engineering community. Applying SDLC practices to create and release robust software. You'll come from either an Engineering or Data Science background, with a good understanding of the Data Science Toolkit (Programming, Machine Learning, MLOps etc) and bringing data science solutions into production. You therefore tick the majority of the following points: Key Requirements A higher degree in engineering, computer science, maths or science. Customer focus with the right balance between outcome delivery and technical excellence. The ability to apply technical skills and know how to solving real world business problems. Demonstratable experience of building scalable and resilient systems. Commercial experience contributing to the success of high impact Data Science projects within complex organisations. Awareness of emerging MLOps practices and tooling would be an advantage e.g. feature stores and model lifecycle management. An analytical mind set and the ability to tackle specific business problems. Experience with different programming languages and a good grasp of at least one language. The ideal candidate is fluent in Python. Use of version control (Git) and related software lifecycle tooling. Experience with tooling for monitoring, logging and alerting e.g. Splunk or Grafana. Understanding of common data structures and algorithms. Experience working with open-source Data Science environments. Knowledge of open source big data technologies such as Apache Spark. Experience building solutions that run in the cloud, ideally Azure. Experience with software development methodologies including Scrum & Kanban. Working patterns We're a big business and we can offer a range of diverse full time & part time working patterns across our many business areas, which means that we can find something that works for you. We work in a more blended pattern - combining office and remote working. Our offices will continue to be where we connect, collaborate and innovate. If you are applying internally, please speak to the Hiring Manager about how this can work for you - Everyone is welcome at Tesco.
Position Overview We are looking for a Senior Machine Learning Engineer (Natural Language Processing) to join our team. You will be an early shaper of the design and architecture of the ML/AI technology at the core of our platform, and work closely with the founding team to deliver 10x value to our clients. As an early-stage startup, we pride ourselves on our ability to roll up our sleeves and build from scratch. You'll ship code frequently, iterate quickly based on feedback, and still meet the stringent security, performance, and uptime requirements our clients expect. We all love to build, scale, and grow: our leadership team has a strong track record of building products, scaling teams, and driving growth, and our ambitious engineers join us from leading tech companies and unicorns. If you're passionate about turning complex problems into simple, elegant solutions, this role is for you. You'll have a meaningful opportunity to shape our engineering culture from the ground up. What you'll do Develop ML/AI functionality at the core of our platform, owning end-to-end design and implementation Solve abstract, open-ended problems using modern AI tools and models across the entire data and engineering stack, working closely with a highly capable team Train, evaluate, and deploy models for intelligent document processing, web data parsing, classification, search, and matching Design and run experiments to explore new approaches, models, and data usage that improve our agents/LLMs, balancing innovation and pragmatism and clear trade-offs Ensure our AI products evolve quickly and safely by applying strong development and testing practices Continuously improve our data science and AI practices, helping define standards, processes, and tooling that align with industry best practices What you bring You are an effective and self-driven builder 3+ years of experience as a machine learning/AI engineer in product-centric settings Experience in production-level Python Experience implementing AI projects (prompt engineering, fine-tuning, multi-agent frameworks, LLMs, conversational AI) Strong understanding of modern AI tools and approaches Bachelor's or Master's degree in Computer Science or a related field Excellent communication skills and ability to work collaboratively in a fast-moving environment Our Offer Tackle meaningful problems with real-world impact Competitive salary and equity package Join us onsite in our offices in London or Porto Flat hierarchy with direct access to founding team Top-of-the-line equipment Private health insurance, plus sick and compassionate leave as needed About us Condukt is the next-generation compliance platform for financial services, offering an agentic solution powered by real-time data for business identity. Always on and policy aware, Condukt enables financial institutions and fintechs to gain actionable insights into business verification - unlocking growth, increasing efficiency, and strengthening compliance. We were founded by former operators and builders with deep experience in banking, payments, and lending. Today, our team spans across London and Porto, bringing together engineers, product owners, and operators from companies like Revolut, Meta, Block (Square), SumUp, American Express, Yandex, and Marshmallow. Trusted by leading regulated entities, including Wise, Tide, Mollie, Shift4, Flatpay, and myPOS, Condukt is backed by top investors, including Lightspeed Venture Partners and MMC Ventures. If you have any questions, please reach out to .
16/05/2026
Full time
Position Overview We are looking for a Senior Machine Learning Engineer (Natural Language Processing) to join our team. You will be an early shaper of the design and architecture of the ML/AI technology at the core of our platform, and work closely with the founding team to deliver 10x value to our clients. As an early-stage startup, we pride ourselves on our ability to roll up our sleeves and build from scratch. You'll ship code frequently, iterate quickly based on feedback, and still meet the stringent security, performance, and uptime requirements our clients expect. We all love to build, scale, and grow: our leadership team has a strong track record of building products, scaling teams, and driving growth, and our ambitious engineers join us from leading tech companies and unicorns. If you're passionate about turning complex problems into simple, elegant solutions, this role is for you. You'll have a meaningful opportunity to shape our engineering culture from the ground up. What you'll do Develop ML/AI functionality at the core of our platform, owning end-to-end design and implementation Solve abstract, open-ended problems using modern AI tools and models across the entire data and engineering stack, working closely with a highly capable team Train, evaluate, and deploy models for intelligent document processing, web data parsing, classification, search, and matching Design and run experiments to explore new approaches, models, and data usage that improve our agents/LLMs, balancing innovation and pragmatism and clear trade-offs Ensure our AI products evolve quickly and safely by applying strong development and testing practices Continuously improve our data science and AI practices, helping define standards, processes, and tooling that align with industry best practices What you bring You are an effective and self-driven builder 3+ years of experience as a machine learning/AI engineer in product-centric settings Experience in production-level Python Experience implementing AI projects (prompt engineering, fine-tuning, multi-agent frameworks, LLMs, conversational AI) Strong understanding of modern AI tools and approaches Bachelor's or Master's degree in Computer Science or a related field Excellent communication skills and ability to work collaboratively in a fast-moving environment Our Offer Tackle meaningful problems with real-world impact Competitive salary and equity package Join us onsite in our offices in London or Porto Flat hierarchy with direct access to founding team Top-of-the-line equipment Private health insurance, plus sick and compassionate leave as needed About us Condukt is the next-generation compliance platform for financial services, offering an agentic solution powered by real-time data for business identity. Always on and policy aware, Condukt enables financial institutions and fintechs to gain actionable insights into business verification - unlocking growth, increasing efficiency, and strengthening compliance. We were founded by former operators and builders with deep experience in banking, payments, and lending. Today, our team spans across London and Porto, bringing together engineers, product owners, and operators from companies like Revolut, Meta, Block (Square), SumUp, American Express, Yandex, and Marshmallow. Trusted by leading regulated entities, including Wise, Tide, Mollie, Shift4, Flatpay, and myPOS, Condukt is backed by top investors, including Lightspeed Venture Partners and MMC Ventures. If you have any questions, please reach out to .
Quotient Sciences: Molecule to Cure. Fast. Quotient Sciences is a leading drug development and manufacturing accelerator, helping biotech and pharma companies bring new medicines to patients faster. With over 35 years of experience and a track record of success, we provide Drug Product (CDMO) and Clinical (CRO) services across the entire development pathway, including formulation development, clinical pharmacology, clinical trials, and commercial product manufacturing. Our proprietary and disruptive platform - "Translational Pharmaceutics " - integrates Drug Product Manufacturing and Clinical Testing to eliminate silos in the drug development process. This in turn reduces costs, improves outcomes, and significantly accelerates drug development times. Why join us: Because every day counts when bringing new medicines to patients. Our 1,000+ experts across the US, UK, and beyond are united by science, agility, and a culture that turns ideas into impact-fast. About the role Quotient Sciences is a drug development accelerator, helping to shorten timelines and bring new treatments to patients faster through our Translational Pharmaceutics platform. As an AI ML Engineer, you will own the full AI lifecycle-from data ingestion through model development, deployment, and monitoring. You'll build and maintain the technical foundations that enable delivery of AI products aligned with our strategic objectives. Recognised internally as a technical expert, you will ensure responsible AI practices, model governance, and compliance, while collaborating with product managers, data engineers, analysts, and business stakeholders to translate requirements into robust AI solutions. Main responsibilities Design, develop, and deploy AI and machine learning models to solve business problems and deliver measurable value. Test and select modelling approaches balancing performance, interpretability, and operational fit; tune/retrain models as needed. Build and maintain scalable ML pipelines and infrastructure for classical ML and deep learning. Deploy models to production using containerisation, CI/CD, and MLOps toolsets; manage ongoing configuration and administration. Develop LLM-based tools using prompt engineering, retrieval, and embedding pipelines for knowledge retrieval and workflow assistance. Build APIs, microservices, or workflow components to integrate AI tools into existing systems. Set up monitoring for model drift, performance, latency, and failures; maintain logging and observability standards. Embed responsible AI practices, governance, and compliance in all solutions; follow GxP and validation standards where required. Collaborate with cross-functional teams to translate business requirements into technical solutions. Produce clear documentation for models, pipelines, deployment steps, and operational expectations. Communicate complex technical concepts in clear, actionable terms to technical and non-technical stakeholders. Mentor and coach team members; foster a collaborative, high-performance culture. Stay current with advancements in AI/ML and data engineering; help shape common frameworks and best practices across the organisation. Skills required Demonstrable experience in AI engineering, machine learning, or data science roles. Proven track record of building, deploying, and maintaining production-grade AI models and pipelines. Strong proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms and ML infrastructure (AWS SageMaker, MLflow). Practical understanding of monitoring, logging, and CI/CD. Experience with LLMs, vector search, or retrieval-augmented systems. Comfortable working with structured and unstructured data. Familiarity with responsible AI practices, data governance, and compliance frameworks. Applied knowledge of Agile principles (Kanban, Scrum) and roadmap delivery using tools like Jira. Excellent communication skills; able to explain complex concepts to non-technical audiences. Previous exposure to life sciences, biotech, or manufacturing desirable; awareness of CDMO processes and GxP/regulatory environments beneficial Application Requirements When applying for a position with Quotient Sciences to be able to work in our organization you must be aged 18 years or over and not have been debarred by the FDA. If you indicate you are under the age of 18 or have been debarred then your application will be automatically declined. Our Commitment to Diversity, Equity and Inclusion Quotient Sciences are advocates for positive change and conscious inclusion. We strive to create a diverse Quotient workforce and develop a workplace culture that provides a sense of acceptance for every person within our organization. As a global employer, we recognize the value in having an organization that is a true reflection and representation of our society today. Specifically we will not discriminate on the basis of race, color, creed, religion, gender, gender identity, pregnancy, marital status, partnership status, domestic violence victim status, sexual orientation, age, national origin, alienage or citizenship status, veteran or military status, disability, medical condition, genetic information, caregiver status, unemployment status or any other characteristic prohibited by federal, state and/or local laws. This applies to all aspects of employment, including hiring, promotion, demotion, compensation, training, working conditions, transfer, job assignments, benefits, layoff, and termination.
14/05/2026
Full time
Quotient Sciences: Molecule to Cure. Fast. Quotient Sciences is a leading drug development and manufacturing accelerator, helping biotech and pharma companies bring new medicines to patients faster. With over 35 years of experience and a track record of success, we provide Drug Product (CDMO) and Clinical (CRO) services across the entire development pathway, including formulation development, clinical pharmacology, clinical trials, and commercial product manufacturing. Our proprietary and disruptive platform - "Translational Pharmaceutics " - integrates Drug Product Manufacturing and Clinical Testing to eliminate silos in the drug development process. This in turn reduces costs, improves outcomes, and significantly accelerates drug development times. Why join us: Because every day counts when bringing new medicines to patients. Our 1,000+ experts across the US, UK, and beyond are united by science, agility, and a culture that turns ideas into impact-fast. About the role Quotient Sciences is a drug development accelerator, helping to shorten timelines and bring new treatments to patients faster through our Translational Pharmaceutics platform. As an AI ML Engineer, you will own the full AI lifecycle-from data ingestion through model development, deployment, and monitoring. You'll build and maintain the technical foundations that enable delivery of AI products aligned with our strategic objectives. Recognised internally as a technical expert, you will ensure responsible AI practices, model governance, and compliance, while collaborating with product managers, data engineers, analysts, and business stakeholders to translate requirements into robust AI solutions. Main responsibilities Design, develop, and deploy AI and machine learning models to solve business problems and deliver measurable value. Test and select modelling approaches balancing performance, interpretability, and operational fit; tune/retrain models as needed. Build and maintain scalable ML pipelines and infrastructure for classical ML and deep learning. Deploy models to production using containerisation, CI/CD, and MLOps toolsets; manage ongoing configuration and administration. Develop LLM-based tools using prompt engineering, retrieval, and embedding pipelines for knowledge retrieval and workflow assistance. Build APIs, microservices, or workflow components to integrate AI tools into existing systems. Set up monitoring for model drift, performance, latency, and failures; maintain logging and observability standards. Embed responsible AI practices, governance, and compliance in all solutions; follow GxP and validation standards where required. Collaborate with cross-functional teams to translate business requirements into technical solutions. Produce clear documentation for models, pipelines, deployment steps, and operational expectations. Communicate complex technical concepts in clear, actionable terms to technical and non-technical stakeholders. Mentor and coach team members; foster a collaborative, high-performance culture. Stay current with advancements in AI/ML and data engineering; help shape common frameworks and best practices across the organisation. Skills required Demonstrable experience in AI engineering, machine learning, or data science roles. Proven track record of building, deploying, and maintaining production-grade AI models and pipelines. Strong proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms and ML infrastructure (AWS SageMaker, MLflow). Practical understanding of monitoring, logging, and CI/CD. Experience with LLMs, vector search, or retrieval-augmented systems. Comfortable working with structured and unstructured data. Familiarity with responsible AI practices, data governance, and compliance frameworks. Applied knowledge of Agile principles (Kanban, Scrum) and roadmap delivery using tools like Jira. Excellent communication skills; able to explain complex concepts to non-technical audiences. Previous exposure to life sciences, biotech, or manufacturing desirable; awareness of CDMO processes and GxP/regulatory environments beneficial Application Requirements When applying for a position with Quotient Sciences to be able to work in our organization you must be aged 18 years or over and not have been debarred by the FDA. If you indicate you are under the age of 18 or have been debarred then your application will be automatically declined. Our Commitment to Diversity, Equity and Inclusion Quotient Sciences are advocates for positive change and conscious inclusion. We strive to create a diverse Quotient workforce and develop a workplace culture that provides a sense of acceptance for every person within our organization. As a global employer, we recognize the value in having an organization that is a true reflection and representation of our society today. Specifically we will not discriminate on the basis of race, color, creed, religion, gender, gender identity, pregnancy, marital status, partnership status, domestic violence victim status, sexual orientation, age, national origin, alienage or citizenship status, veteran or military status, disability, medical condition, genetic information, caregiver status, unemployment status or any other characteristic prohibited by federal, state and/or local laws. This applies to all aspects of employment, including hiring, promotion, demotion, compensation, training, working conditions, transfer, job assignments, benefits, layoff, and termination.
Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require. The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations. The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting edge research to real world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in house energy analysts and traders and domain experts to ensure reliability of our predictions. You'll be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you'll help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault tolerance of every component of our ML platform. You Are: Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost Skilled in developing classification models and anomaly detection systems for production environments Capable of implementing comprehensive data lineage tracking and model governance systems Driven by working in an intellectually engaging environment with top energy analysts and traders and technology experts, where constructive challenges and technical debates are encouraged Excited about working in a dynamic environment: not afraid of complex energy challenges, eager to bring new ML innovations to production, and a positive can do attitude Passionate about mentoring team members, helping them improve their ML engineering skills and grow their careers Experienced with the full ML model lifecycle, including experiment design, model development, validation, deployment, monitoring, and maintenance Awesome If You: Have experience in the energy sector or understanding of energy systems and operations Have practical experience with AWS services (SageMaker, S3, EC2, Lambda, etc.) Have experience with infrastructure as code tools (Terraform, CloudFormation) Have experience with Apache Kafka and real time streaming frameworks Are familiar with observability principles such as logging, monitoring, and distributed tracing for ML systems Have experience with transformer architectures and generative AI applications in operational contexts Have experience with time series analysis and forecasting techniques relevant to energy applications Are knowledgeable about data privacy regulations and compliance frameworks in the energy sector Benefits Enjoy flexible hybrid working - split your time between home and our office, with the freedom to work where you're most productive. A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge A team of motivated characters and top minds striving to be the best at what we do at all times Constantly learning and exploring new tools and technologies Acting as company owners (all Vortexa staff have equity options)- in a business savvy and responsible way Motivated by being collaborative, working and achieving together Private Health Insurance offered via Vitality to help you look after your physical health Global Volunteering Policy to help you 'do good' and feel better
11/05/2026
Full time
Processing thousands of energy data points per second from diverse operational sources, handling massive volumes of energy data while running sophisticated classification and anomaly detection models in real-time, maintaining comprehensive data lineage, and delivering insights through high-performance platforms used by energy operators globally requires exceptional engineering and scientific expertise. This processing demands models that can withstand the scrutiny of energy analysts and traders, operations teams, and regulatory bodies, with the performance, stability, and reliability that critical energy systems require. The Data Platform Team is responsible for all machine learning operations across our energy data ecosystem. We work with everything from raw sensor data from millions of energy assets to complex operational datasets, generating high-value predictions such as equipment failure detection, energy demand forecasting, operational anomaly identification, predictive maintenance scheduling, and system optimization recommendations. The team has built a comprehensive suite of statistical and machine learning models that enable us to provide the most accurate and actionable insights into energy operations. We take pride in applying cutting edge research to real world energy challenges in a robust, scalable, and maintainable way. The quality of our models is continuously validated by experienced in house energy analysts and traders and domain experts to ensure reliability of our predictions. You'll be instrumental in designing and building ML infrastructure and applications to propel the design, deployment, and monitoring of existing and new ML pipelines and models. Working with software engineers, data scientists, and energy analysts and traders, you'll help bridge the gap between research experiments and production energy systems by ensuring 100% uptime and bulletproof fault tolerance of every component of our ML platform. You Are: Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost Skilled in developing classification models and anomaly detection systems for production environments Capable of implementing comprehensive data lineage tracking and model governance systems Driven by working in an intellectually engaging environment with top energy analysts and traders and technology experts, where constructive challenges and technical debates are encouraged Excited about working in a dynamic environment: not afraid of complex energy challenges, eager to bring new ML innovations to production, and a positive can do attitude Passionate about mentoring team members, helping them improve their ML engineering skills and grow their careers Experienced with the full ML model lifecycle, including experiment design, model development, validation, deployment, monitoring, and maintenance Awesome If You: Have experience in the energy sector or understanding of energy systems and operations Have practical experience with AWS services (SageMaker, S3, EC2, Lambda, etc.) Have experience with infrastructure as code tools (Terraform, CloudFormation) Have experience with Apache Kafka and real time streaming frameworks Are familiar with observability principles such as logging, monitoring, and distributed tracing for ML systems Have experience with transformer architectures and generative AI applications in operational contexts Have experience with time series analysis and forecasting techniques relevant to energy applications Are knowledgeable about data privacy regulations and compliance frameworks in the energy sector Benefits Enjoy flexible hybrid working - split your time between home and our office, with the freedom to work where you're most productive. A vibrant, diverse company pushing ourselves and the technology to deliver beyond the cutting edge A team of motivated characters and top minds striving to be the best at what we do at all times Constantly learning and exploring new tools and technologies Acting as company owners (all Vortexa staff have equity options)- in a business savvy and responsible way Motivated by being collaborative, working and achieving together Private Health Insurance offered via Vitality to help you look after your physical health Global Volunteering Policy to help you 'do good' and feel better
We're looking for a Machine Learning Engineer, junior to mid-level, to come join our impact-focused team. If you'd like to use your skills to create real-world solutions that lower carbon emissions and accelerate the energy transition, then this is the role for you. We're a friendly, supportive and fun team who care about making a difference. Sound like a role you're interested in? Take a look at an overview of the requirements below Skills in Python and Git, in particular PyTorch, and a passion for ML experimentation. 1-4 years of experience in developing deep learning models in industry or postgraduate academia. Hybrid working: 2 days in our London office. £40,000 - £60,000 (based on experience) Applications are accepted on a rolling basis, so we encourage you to apply ASAP. Unfortunately, we are unable to offer visa sponsorship for this role. At OCF, your values and passion are more important to us than if you meet every requirement listed. If this role excites you and your values are aligned, we strongly encourage you to apply. You might be exactly who we're looking for! Apply now via this link
09/05/2026
Full time
We're looking for a Machine Learning Engineer, junior to mid-level, to come join our impact-focused team. If you'd like to use your skills to create real-world solutions that lower carbon emissions and accelerate the energy transition, then this is the role for you. We're a friendly, supportive and fun team who care about making a difference. Sound like a role you're interested in? Take a look at an overview of the requirements below Skills in Python and Git, in particular PyTorch, and a passion for ML experimentation. 1-4 years of experience in developing deep learning models in industry or postgraduate academia. Hybrid working: 2 days in our London office. £40,000 - £60,000 (based on experience) Applications are accepted on a rolling basis, so we encourage you to apply ASAP. Unfortunately, we are unable to offer visa sponsorship for this role. At OCF, your values and passion are more important to us than if you meet every requirement listed. If this role excites you and your values are aligned, we strongly encourage you to apply. You might be exactly who we're looking for! Apply now via this link
