25 jobs found

Company Overview Circadia Health is a medical device and data technology company that has developed the world's first FDA cleared contactless remote patient monitoring system. Powered by cutting-edge technology and AI, the system allows for the early detection of medical events such as Congestive Heart Failure, COPD Exacerbations, Pneumonia, Sepsis, UTIs, and Falls. We're monitoring over 28,000+ lives daily and growing rapidly. As we scale our team, Circadia is looking for energetic, personable, and solutions-oriented individuals driven by creating the ultimate customer experience. Prior experience in healthcare is a big plus, but not required. Our mission is to enhance patient outcomes and improve healthcare processes by providing cutting-edge solutions to healthcare providers and patients alike. Position Overview As a Machine Learning Engineer, you will design, build, and maintain end-to-end machine learning pipelines, transforming experimental models into scalable, production-ready systems while closely collaborating with the Product Design and Engineering (PDE) team to create impactful ML-driven products in the healthcare setting. In addition to optimizing infrastructure, automating workflows, and ensuring seamless integration from model development to deployment, you will play a key role in building and iterating on the actual products that leverage machine learning to deliver value to patients and healthcare professionals. With a strong focus on scalability and performance, you will help bridge the gap between cutting-edge algorithms and real-world applications in a fast-paced, startup environment - driving our mission of saving lives. Key Responsibilities: Ownership of Machine Learning Infrastructure: Develop, deploy, and maintain scalable pipelines for both Circadia's proprietary ML models and off-the-shelf solutions. Optimize model training and inference workflows to handle large-scale, real-time data efficiently. Design robust model monitoring systems to track performance, detect drift, and ensure reliability. Implement infrastructure to support the experimentation and productionization of ML models cost-effectively in AWS and Snowflake. Building and Deploying ML-Driven Products: Collaborate closely with the Product Design and Engineering (PDE) team to design, build, and iterate on ML-powered products. Translate complex machine learning algorithms into user-facing features and services. Work with key stakeholders to ensure alignment between technical implementation and product goals. Define and develop APIs for seamless integration of ML models with product functionalities. Orchestration of Scalable ML Pipelines: Engage with data and ML scientists to plan the architecture for end-to-end machine learning workflows. Implement scalable training and deployment pipelines using tools such as Apache Airflow and Kubernetes. Perform comprehensive testing to ensure reliability and accuracy of deployed models. Develop instrumentation and automated alerts to manage system health and detect issues in real-time. Attributes: Technical acumen: Mastery of computer science fundamentals and understanding of core machine learning concepts. Detail oriented: Responsible for mission-critical healthcare machine learning models. Communications and Trust: Good communication skills with the ability to liaise with both technical and non-technical stakeholders. Organization and Getting Stuff Done: Juggling multiple projects and timelines. Prioritizing. Keen eye for detail in all tasks and projects. Growth Mindset: Your ability to learn from mistakes, reflect on mistakes, and not make mistakes again. Being curious and asking questions and showing resilience in the face of setbacks. Benefits: Join an energetic, diverse team dedicated to working towards the challenge of improving and saving patient lives. Private health insurance with Vitality Health for you and your family, including discounted gym memberships, wellness retreats, fitness devices, and lots more. 28 days paid annual leave during each holiday year (including bank holidays). Fully financed learning and personal development courses to help you grow in your role. Opportunity to attend conferences and acquire certifications, paid for by the company. New laptop of your choice for you to work on either at home or at Circadia's London Bridge office. Flexible / hybrid working to suit your personal circumstances and allow you to be productive wherever you are most comfortable working. Participate in and help plan regular team events, lunches, and dinners.

About Us At Encord, we're building the AI infrastructure of the future. The biggest challenge AI companies face today is not half as glamorous as the outside world may think: it's all about data quality. In fact, the success of any AI application today relies on the quality of a model's training data - and for 95% of teams, this essential step is both the most costly and the most time-consuming in getting their product to market. As ex-computer scientists, physicists, and quants, we felt first-hand how the lack of tools to prepare quality training data was impeding the progress of building AI. AI today is what the early days of computing or the internet were like, where the potential of the technology is clear, but the tools and processes surrounding it are still primitive, preventing the next generation of applications. This is why we started Encord. We are a talented and ambitious team of 75+, working at the cutting edge of computer vision and deep learning, backed by top investors, including CRV and Y Combinator, leading industry executives like Luc Vincent, former VP of AI at Meta, and other top Bay Area leaders in AI. We are one of the fastest growing companies in our space and consistently rated as the best tool in the market by our customers. We have big plans ahead and are looking for a Machine Learning Engineer to join our ML team. The Role We are looking for an experienced Machine Learning Engineer to help us conduct research on the state of the art of computer vision and solve multifaceted algorithmic problems. You will: Experiment with and adapt latest ML technologies to fit into existing tech stack Solve idiosyncratic statistical, geometric, and engineering problems Work closely with a full stack tech team to assist implementation of research solutions into the product Contribute to hiring additional talent to our rapidly growing team The role will be exposed to a broad tech stack (e.g. ReactJS, Python, REST & GraphQL, OpenCV, PyTorch, GCP, AWS & CUDA, Kubernetes) and the cutting edge of computer vision and deep learning. Qualifications The right candidate will have a proven track record of relevant publications and previous experience managing applied research teams. Requirements for the role include: Passion for solving ML problems Strong experience in Python and machine learning libraries such as OpenCV, PyTorch, TensorFlow, Fast.ai , and Keras Strong experience in mathematical programming, algorithmic problem solving, and applied machine learning What We Offer Competitive salary, commission, and equity in a hyper growth business. Strong in-person culture: most of our team is in the office 3+ days a week. Room to grow into anything you choose to - a year ago we were 25 people, now we're 60. We'll be growing insanely fast over the next 24 months and you'll have all the opportunities for growth as you can handle. 25 days annual leave a year + public holidays. Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services enterprise AI use cases across many different industry verticals such as healthcare, surveillance, retail, agriculture, and many more. Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields.

As a Machine Learning Engineer in the Applied AI team at Graphcore, you will contribute to advancing AI technology by developing and optimising AI models tailored to our specialised hardware. Working closely with the Software development and Research teams, you will play a critical role in finding opportunities to innovate and differentiate Graphcore's technology. We seek engineers with strong technical skills and an understanding of AI model implementation, eager to make a tangible impact in this rapidly evolving field. The Team The Applied AI team's role is to be proxies for our customers, we need to understand the latest AI models, applications, and software to ensure that Graphcore's technology works seamlessly with the AI ecosystem. We build reference applications, contribute to key software libraries e.g. optimising kernels for efficiency on our hardware, and collaborate with the Research team to develop and publish novel ideas in domains such as efficient compute, model scaling and distributed training and inference of AI models for multiple modalities and applications. If you're excited about advancing the next generation of AI models on cutting-edge hardware, we'd love to hear from you! Responsibilities and Duties Implement the latest machine learning models and optimise them for performance and accuracy, scaling to 1000s of accelerators. Test and evaluate new internal software releases, provide feedback to software engineering teams, make vital code fixes, and conduct code reviews. Benchmark models and key ML techniques to identify performance bottlenecks and improve model efficiency. Design and conduct experiments on novel AI methods, implement them and evaluate results. Collaborate with Research, Software, and Product teams to define, build, and test Graphcore's next generation of AI hardware. Engage with AI community and keep in touch with the latest developments in AI. Candidate Profile Essential skills: Bachelor/Master's/PhD or equivalent experience in Machine Learning, Computer Science, Maths, Data Science, or related field. Proficiency in deep learning frameworks like PyTorch/JAX. Strong Python software development skills (nice to have C++/other languages). Familiar with deep learning fundamentals: models, optimisation, evaluation and scaling. Capable of designing, executing and reporting from ML experiments. Ability to move quickly in a dynamic environment. Enjoy cross-functional work collaborating with other teams. Strong communicator - able to explain complex technical concepts to different audiences. Desirable: Experience in one or more of: distributed training of large-scale ML models, building production systems with large language models, efficient computing based on low-precision arithmetic, deep learning models including large generative models for language, vision and other modalities . Experience writing C++/Triton/CUDA kernels for performance optimisation of ML models. Have contributed to open-source projects or published research papers in relevant fields. Knowledge of cloud computing platforms. Keen to present, publish and deliver talks in the AI community. Benefits In addition to a competitive salary, Graphcore offers flexible working, a generous annual leave policy, private medical insurance and health cash plan, a dental plan, pension (matched up to 5%), life assurance and income protection. We have a generous parental leave policy and an employee assistance programme (which includes health, mental wellbeing, and bereavement support). We offer a range of healthy food and snacks at our central Bristol office and have our own barista bar! We welcome people of different backgrounds and experiences; we're committed to building an inclusive work environment that makes Graphcore a great home for everyone. We offer an equal opportunity process and understand that there are visible and invisible differences in all of us. We can provide a flexible approach to interview and encourage you to chat to us if you require any reasonable adjustments.

Team: Tech Hours: Full time Reports to: Defect Detection Team Lead Location: Exeter or Bristol, minimum of 3 days in the office. Role Overview: As a Machine Learning Engineer at DeGould you will be responsible for building and maintaining our labelling, training and production inference data pipelines to produce high quality datasets, models and services to power our automated vehicle inspection product. Following MLOps and DevOps best practices you will build and deploy bespoke computer vision ML models using a service-oriented architecture in AWS, GCP and on Edge to process photos from DeGould's ultra high-resolution imaging photo booths. The objective is to convert this data into useful information that creates value for customers. DeGould is an exciting, multi-award-winning company, in the software and AI sector. The company develops and delivers innovative vision and damage detection systems to a range of blue-chip corporate clients (including Toyota, Ford, Jaguar Land Rover, Mercedes Benz, Nissan, Honda and Bentley). As the company embarks on an exciting growth phase, it plans to expand the team, further develop existing products, and explore opportunities for new ones. Our Vision: DeGould's vision is to be the standard for new vehicle inspection in the automotive sector. Key Responsibilities: The main deliverables of the role are: Deliver performant machine learning models for customers. Building capabilities to monitor and evaluate model performance and metrics. Detailed duties of the role include: Write production, robust, readable and extendable code to support machine learning pipelines. Use industry best practices and seek to implement improvements across the machine learning lifecycle. Continuous evaluation of models in production and system performance analysis. Proactively seek technical solutions that solve customer problems. Stay up to date and evaluate opportunities to apply the latest tools, research, methods and technologies. Work across multidisciplinary teams to deliver against the company's objectives. Interpret internal and external business challenges and recommend appropriate system and technology solutions to produce a functional solution. Identify areas for improvement and development using a full range of software development tools. Undertaking any other tasks/duties as may be reasonably required to fulfil DeGould's objectives. Depending on the individual role, some or all of the following: Developing and championing robust MLOps frameworks and policies. Training and maintaining performant vehicle segmentation models. Labelling tasks and data quality. Designing and implementing reporting dashboards. Developing novel approaches from academic and industry research. Production model deployment and maintenance. Skills: Technical expertise in AI for image processing using: deep learning, machine learning, transfer learning, CNNs and transformers, such as Detectron, ConvNext, DETR, DINO or similar. Technical knowledge of relevant ML performance metrics and how to apply them to monitor models. Strong knowledge of Python (such as numpy, pandas, matplotlib, streamlit, and opencv). Strong knowledge of modern programming paradigms (OOP, functional programming etc). Ability to write clean, robust, readable, error handling and error tolerant code. Good knowledge of at least one of PyTorch, Keras, or Tensorflow. Working knowledge of core AWS concepts and services such as EC2, ECS, EKS, and DynamoDB. Good knowledge of DevOps and MLOps tools, including usage of Git, Bash, UNIX, Docker, containers and CI/CD pipelines (GitHub Actions or similar). Able to work effectively both as part of a team and individually. Behaviours: As an employee of DeGould Ltd, you are required to meet a number of common standards of behaviour, accountabilities and outcomes. In addition, and in relation to this role it is expected that the successful candidate will exhibit these behaviours: Leadership - leads by example through their own behaviour. Creative - open to new ideas and unafraid to try new approaches. Analytical - capable of working through detail and uses data in decision making. Flexibility - thriving in a fast paced, changing and opportunity rich environment. Collaborative - enthusiastically works with colleagues and customers alike. Dependable - deliver on stakeholder commitments in a timely manner. Benefits: Competitive salary and benefits including: 25 days holiday per annum (excluding bank holidays). Additional days holiday for birthday. Cycle to work scheme. Pension auto enrolment after 3 months service. Enhanced maternity, paternity and shared parental leave. Health insurance with Vitality for employee, spouse and children. Flexible working can be agreed.

Machine Learning Engineer (Contract) - £550/£600 PD (Inside IR35) An excellent opportunity has arisen with a global brand for a Contract Machine Learning Engineer. This role focuses on natural language processing and network analysis, leveraging open-source software in a highly collaborative environment. You'll contribute to building, maintaining, and enhancing their data science capabilities within the organization. Role and Responsibilities: Design, develop, and evaluate machine learning models, with a focus on natural language processing Translate business challenges into data science solutions and communicate complex findings to stakeholders Collaborate in a cross-functional team by sharing ideas, conducting code reviews, and enhancing internal data science tools Identify opportunities where machine learning can support data-driven decision making Foster a diverse and inclusive culture by working across departments and contributing to knowledge sharing Essential Skills & Experience: Strong proficiency in Python and Graph Databases Experience with core data science libraries such as Scikit-Learn, Pandas, PyTorch/TensorFlow Solid understanding of machine learning and NLP techniques Familiarity with version control systems (Git/GitHub), Python package management, unit testing, and code reviews Hands-on experience with data exploration and visualization tools like Pandas, Matplotlib, Bokeh, Plotly, or Tableau Exposure to cloud platforms such as AWS Experience working collaboratively in multidisciplinary teams alongside machine learning and software engineers Contract Terms: • London/Hybrid • Inside IR35 • 6 Months • Rate: £550/£600 PD Company: IntecSelect Qualifications: Senior (5+ years of experience)

Are you a Machine Learning Engineer looking to shape the future of game development? We are looking for a Machine Learning Engineer to join a talented team, using cutting-edge AI to create innovative and immersive game worlds. If you have experience in machine learning, generative models, and software engineering , this is an opportunity to work on projects enjoyed by millions. Key Responsibilities: Develop and implement machine learning-driven features to generate dynamic game worlds Contribute to a collaborative, ambitious, and creative development team Design and iterate on new features, incorporating feedback from peers Work independently while effectively managing priorities and workload What You'll Need: Strong software engineering skills Experience in machine learning engineering , with proficiency in modern libraries such as PyTorch Deep understanding of generative diffusion models and VAEs Ability to work autonomously, identifying and prioritising key tasks Strong communication skills and the ability to collaborate within a team Adaptability and flexibility to work in a fast-changing environment If you are passionate about AI-driven game development and ready to make an impact, apply now through Alfie at Skillsearch:

Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life. Diversity at Altos We believe that diverse perspectives are foundational to scientific innovation and inquiry. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment. What You Will Contribute To Altos Altos Labs is building high-performance, scalable, quantitative solutions for biomedical image analysis and integration with multi-Omics data. The team works at multiple scales including data from Electron/Light Microscopy, Digital Histology and Pathology up to functional analysis In Vivo. We will enable and accelerate the Altos mission by leveraging state of the art computer vision and machine learning, and collaborating with MLOps at Altos to make all our models easily trainable, findable, interpretable, and accessible across diverse research groups. Responsibilities Evaluate state of the art and retrain AI models across the full spectrum of imaging including: de novo protein design, structure identification and dynamics in single particle CryoEM; light microscopy and multi-omics data integration and cross domain mapping of data collected in situ and in vivo. Demonstrate software engineering skills to develop reliable, scalable, performant distributed systems in a cloud environment. Develop efficient data loading strategy and performance tracking to train large models with distributed training across multiple nodes. Build, deploy, and manage multi modal analysis pipelines for scientific analysis, and machine learning workflows in an integrated, usable framework. Understand scientists' needs across a wide range of scientific disciplines by collaborating with both users and software engineers. Bridge the communication gap between experimental scientists, algorithm developers, and software deployers. Who You Are Minimum Qualifications BS/MS in Computer Science/Biomedical Engineering or related quantitative field. Candidates should have relevant industry and/or academic experience. Experience with one or more programming languages commonly used for large-scale data management and machine learning, such as Python, C++, Pytorch/Tensorflow, Pytorch Lightning, etc. Previous experience with Machine Learning at scale: Large Language Models and Self-Supervised/Contrastive/Representation Learning for Computer Vision applications and multi modal integration. Experience applying software engineering practices in a scientific environment, or another environment with similar characteristics. Demonstrated track record of hands-on technical leadership and scientific contributions such as papers or conference communications. Excited to design and implement technical and cultural standards across scientific and technical functions. Preferred Qualifications Bioinformatics data processing and analysis. Experience with cloud computing and containerization. Knowledge of genetics/human genetics. The salary range for Cambridge, UK : Exact compensation may vary based on skills, experience, and location. What We Want You To Know We are a culture of collaboration and scientific excellence, and we believe in the values of inclusion and belonging to inspire innovation. Altos Labs provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training. Altos currently requires all employees to be fully vaccinated against COVID-19, subject to legally required exemptions (e.g., due to a medical condition or sincerely-held religious belief). Thank you for your interest in Altos Labs where we strive for a culture of scientific excellence, learning, and belonging.

Our vision is to be the universal symbol of trust, bringing consumers and businesses together through reviews. We are well on our way-but there's still an exciting journey ahead. Join us at the heart of trust. As part of the MLOps team, you'll work closely with data scientists, software engineers, and other stakeholders to bring machine learning models to life-ensuring they're deployed, maintained, and monitored efficiently in production. You'll have the opportunity to improve model performance and infrastructure, all while contributing to Trustpilot's AI-based solutions. What you'll be doing: Model Deployment: Collaborate with data scientists to take machine learning models from development to production, ensuring quality work and scalability. Build Pipelines: Develop and maintain data and model pipelines, integrating seamlessly with our existing systems to support reliable, efficient workflows. CI/CD for ML: Design and implement continuous integration and delivery pipelines to streamline the deployment of machine learning models. Model Monitoring: Help monitor the performance of machine learning models post-deployment, ensuring reliability, scalability, and quality over time. Collaboration: Work with cross-functional teams to design solutions that meet business needs while adhering to best practices in machine learning and software engineering. Optimize: Continuously improve our infrastructure, ensuring we remain at the forefront of AI model production and delivery. Who you are: Solid technical foundation in both machine learning and software engineering. Experience deploying machine learning models in production environments. Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with cloud platforms like GCP, AWS, or Azure. Experience with CI/CD pipelines for machine learning (e.g., Vertex AI). Experience with data processing frameworks and tools, particularly Apache Beam/Dataflow is highly desirable. Knowledge of monitoring and maintaining models in production. Proficiency in employing containerization tools, including Docker, to streamline the configuration of deployment settings. Problem-solving skills with the ability to troubleshoot model and pipeline issues. Good communication skills, enabling effective collaboration across teams. What's in it for you: A range of flexible working options to dedicate time to what matters to you. Competitive compensation package + bonus. 25 days holiday per year, increasing to 28 days after 2 years of employment. Two (paid) volunteering days a year to spend your time giving back to the causes that matter to you and your community. Rich learning and development opportunities supported through the Trustpilot Academy, LinkedIn Learning, and Blinkist. Pension and life insurance. Health cash plan, online GP, 24/7 Employee Assistance Plan. Full access to Headspace, a popular mindfulness app to promote positive mental health. Paid parental leave. Season ticket loan and a cycle-to-work scheme. Central office location complete with all the snacks and refreshments you can ask for. Regular opportunities to connect and get to know your fellow Trusties, including company-wide celebrations and events, ERG activities, and team socials. Access to over 4,000 deals and discounts on things like travel, electronics, fashion, fitness, cinema discounts, and more. Still not sure? We want to be a part of creating a more diverse, equitable, and inclusive world of work for all. We're excited to hear about your experiences as well as how you will contribute to our working culture. So, even if you don't feel you meet all the requirements, we'd still really like to hear from you! About us Trustpilot began in 2007 with a simple yet powerful idea that is more relevant today than ever - to be the universal symbol of trust, bringing consumers and businesses together through reviews. Trustpilot is open, independent, and impartial - we help consumers make the right choices and businesses to build trust, grow and improve. Today, we have more than 320 million reviews and 70 million monthly active users across the globe, with 140 billion annual Trustbox impressions, and the numbers keep growing. We have more than 1,000 employees and we're headquartered in Copenhagen, with operations in Amsterdam, Denver, Edinburgh, Hamburg, London, Melbourne, Milan, and New York. We're driven by connection. It's at the heart of what we do. Our culture keeps things fresh it's built on the relationships we create. We talk, we laugh, we collaborate and we respect each other. We work across borders and cultures to be the universal symbol of trust in an ever-changing world. With vibrant office locations worldwide and over 50 nationalities, we're proud to be an equal opportunity workplace with diverse perspectives and ideas. Our purpose to help people and businesses help each other is a tall order, but we keep it real. We're a great bunch of humans, doing awesome stuff, without fuss or pretense. A successful Trustpilot future is driven by you we give you the autonomy to shape a career you can be proud of. If you're ready to grow, let's go. Join us at the heart of trust. Trustpilot is committed to creating an inclusive environment where people from all backgrounds can thrive and where different viewpoints and experiences are valued and respected. Trustpilot will consider all applications for employment without regard to race, ethnicity, national origin, religious beliefs, gender identity or expression, sexual orientation, neurodiversity, disability, age, parental or veteran status. Together, we are the heart of trust. Trustpilot is a global company and our data practices are designed to ensure that your personally identifiable information is appropriately protected. Please note that your personal information will be transferred, accessed, and stored globally as necessary for the uses and disclosures stated in our Privacy Policy.

One of Europe's most exciting Analytics software companies is looking to hire a Machine Learning Engineer. If you are a Machine Learning expert and are interested in working on automation, AI, NLP & Data Visualisation software products for big corporates, then this role might be for you. You will join a rapidly expanding team of experts providing valuable solutions to some of the world's biggest organisations around Risk Management & Cyber Security. Responsibilities: Develop and implement machine learning algorithms. Collaborate with team members to solve complex problems through technical solutions. Contribute to the design and development of software products. Minimum Requirements: Strong STEM degree, ideally MSc level & above. Good knowledge & experience of Machine Learning algorithms. Strong Software Engineering skills - ideally Python. Ability to articulate how you have solved complex problems in the past through technical solutions. You will join a hugely talented team of Machine Learning Engineers, and an employer committed to employee wellbeing. Regular mental health, fitness & yoga sessions are a part of the working day, as well as regular company away days. Apply now to the Machine Learning Engineer role by clicking on the link and uploading either your CV or an Executive Bio.

Relay is a warehouse-to-doorstep delivery network purpose built to scale and adapt to the demands of e-commerce. We help the most important e-commerce retailers in the UK (and one day, the world) to deliver faster, more affordably, and with a smaller carbon footprint than existing solutions. Our success depends on our ability to deliver parcels efficiently (minimising delivery cost, energy expenditure, and greenhouse gas emissions), while maximising quality (minimising lost parcels and maximising on-time delivery). As we scale our network, we will increasingly rely on machine learning to drive these properties. Some typical use cases include Identifying couriers having a tough time on the road, so that our operations team can reach out and help them get back on track Improving our route length predictions, so we can offer courier accurate market pay Extracting features from proof-of-delivery photos and assessing the risk that a parcel delivered to a safe place will be lost or stolen before its recipient can take possession of it. We're looking for an exceptional machine learning engineer to join our engineering team. In this role, you can expect to Build our machine learning engineering discipline from the ground up, deploying models, engineering features, and standing up the infrastructure that makes it all possible Take ownership over mission-critical components in the "brain" of our logistics network Work closely with fellow technologists (data scientists, backend and mobile engineers) as well as with members of our operations teams Regularly spend time in the field learning how the technology you build impacts our couriers and parcel recipients. You might be a great fit for this role if You have deep prior experience with machine learning, but don't just think of yourself as a "machine learning engineer" You are excited to take on a wide range of challenges within machine learning and software engineering You are practical and impact-oriented. You are scientific and rigorous in your work, but you can't stand "science projects" - technology built for its own sake rather than to benefit end users or the business You act with agency and take pride of ownership in your work. You naturally take initiative, seeking out the best opportunities for impact. You have deep empathy for the humans for whom you build technology, including customers, partners, and your fellow colleagues. You seek out the chance to hear directly from them and go out of your way to incorporate their feedback into your work. You are eager to learn new technologies and take on new problem domains. You value and practise clear communication, active listening, and intentional collaboration. We are looking for candidates who Have at least two years of experience deploying models in production and working on machine learning infrastructure Have worked on high-performing teams building software for at least four years. Have broad experience across a variety of technology stacks. What we offer: 25 days annual leave per year (plus bank holidays). Generous equity package. Bupa Global: Business Premier Health Plan - Comprehensive global health insurance with direct access to specialists, dental care, mental health support and more. Contributory pension scheme. Hybrid working in our Dog-friendly co-working space; we're based in London near Old Street tube station. Free membership of the gym in our co-working space in London. Cycle-to-work scheme. A culture of learning and growth, where you're encouraged to take ownership from day one. Plenty of team socials and events - from pottery painting to life-size Monopoly and escape rooms.

About Faculty At Faculty, we transform organisational performance through safe, impactful and human-centric AI. With a decade of experience, we provide over 300 global customers with software, bespoke AI consultancy , and Fellows from our award winning Fellowship programme . Our expert team brings together leaders from across government, academia and global tech giants to solve the biggest challenges in applied AI. Should you join us, you'll have the chance to work with, and learn from, some of the brilliant minds who are bringing Frontier AI to the frontlines of the world. We operate a hybrid way of working You'll split your time across client location, Faculty's Old Street office and working from home depending on the needs of the project. For this role, you can expect to be client-side for up to three days per week at times and working either from home or our Old Street office for the rest of your time. About the Role You will design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. The work you do will help our customers solve a broad range of high-impact problems in our Defence team - examples of which can be found here. Because of the potential to work with our clients in the National Security space, you will need to be eligible for Security Clearance, details of which are outlined when you click through to apply. What You'll Be Doing You are engineering-focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting-edge ML applications into the real world. You will develop new methodologies and champion best practices for managing AI systems deployed at scale, with regard to technical, ethical and practical requirements. You will support both technical, and non-technical stakeholders, to deploy ML to solve real-world problems. Our Machine Learning Engineers are responsible for the engineering aspects of our customer delivery projects. As a Machine Learning Engineer, you'll be essential to helping us achieve that goal by: Building software and infrastructure that leverages Machine Learning; Creating reusable, scalable tools to enable better delivery of ML systems; Working with our customers to help understand their needs; Working with data scientists and engineers to develop best practices and new technologies; Implementing and developing Faculty's view on what it means to operationalise ML software. As a rapidly growing organisation, roles are dynamic and subject to change. Your role will evolve alongside business needs, but you can expect your key responsibilities to include: Working in cross-functional teams of engineers, data scientists, designers and managers to deliver technically sophisticated, high-impact systems; Working with senior engineers to scope projects and design systems; Providing technical expertise to our customers; Technical Delivery. Who We're Looking For You can view our company principles here . We look for individuals who share these principles and our excitement to help our customers reap the rewards of AI responsibly. We like people who combine expertise and ambition with optimism who are interested in changing the world for the better and have the drive and intelligence to make it happen. If you're the right candidate for us, you probably: Think scientifically, even if you're not a scientist - you test assumptions, seek evidence and are always looking for opportunities to improve the way we do things; Love finding new ways to solve old problems - when it comes to your work and professional development, you don't believe in 'good enough'. You always seek new ways to solve old challenges; Are pragmatic and outcome-focused - you know how to balance the big picture with the little details and know a great idea is useless if it can't be executed in the real world. To succeed in this role, you'll need the following - these are illustrative requirements and we don't expect all applicants to have experience in everything (70% is a rough guide): Understanding of, and experience with the full machine learning lifecycle; Working with Data Scientists to deploy trained machine learning models into production environments; Working with a range of models developed using common frameworks such as Scikit-learn, TensorFlow, or PyTorch; Experience with software engineering best practices and developing applications in Python; Technical experience of cloud architecture, security, deployment, and open-source tools ideally with one of the 3 major cloud providers (AWS, GCP or Azure); Demonstrable experience with containers and specifically Docker and Kubernetes; An understanding of the core concepts of probability and statistics and familiarity with common supervised and unsupervised learning techniques; Demonstrable experience of managing/mentoring more junior members of the team; Outstanding verbal and written communication; Excitement about working in a dynamic role with the autonomy and freedom you need to take ownership of problems and see them through to execution. What we can offer you: The Faculty team is diverse and distinctive, and we all come from different personal, professional and organisational backgrounds. We all have one thing in common: we are driven by a deep intellectual curiosity that powers us forward each day. Faculty is the professional challenge of a lifetime. You'll be surrounded by an impressive group of brilliant minds working to achieve our collective goals. Our consultants, product developers, business development specialists, operations professionals and more all bring something unique to Faculty, and you'll learn something new from everyone you meet.

We're committed to fostering an incredible work environment for our employees. Why We Exist Relay's purpose is to elevate and accelerate e-commerce through delivery. Our Vision To be the most beloved delivery company in the world. Our Mission Realising the most efficient delivery network to create the most compelling experience for clients, consumers and relayers. Relay Leadership Our leadership team and advisors have years of experience in logistics and technology. Roles Senior Talent Acquisition Partner - Data (12 month FTC) Talent Acquisition Partner - Business (6 month FTC)

Grow with us. We are looking for a Machine Learning Engineer to work along the end-to-end ML lifecycle, alongside our existing Product & Engineering team. About Trudenty: The Trudenty Trust Network provides personalised consumer fraud risk intelligence for fraud prevention across the commerce and payments ecosystem, starting with first-party and APP fraud prevention. We are at an exciting point in our journey, as we go to market and drive growth of The Trudenty Trust Network. This next chapter of our story is one in which we will drive impact across the commerce ecosystem, create to stay at the leading edge of innovation across the industry whilst building material value for our team (inclusive of shareholders). We are a 10 person seed stage company that has secured partnerships with notable names in the payments and commerce ecosystem, and raised investment from our first choice of partners who align with our values and ambition for the future. Our team is one of exceptional 'outliers'; defined by grit, resilience, creativity in problem solving, intelligence and mastery of our domains. We are also mission-driven and results-oriented. Working with us, you will get the opportunity to do some of the best work of your life and unfold your full potential as a human. We are a remote team, that co-works from London frequently. So easy travel into London should be possible for everyone in our team. The role We are looking for a Machine Learning Engineer with a spike in data engineering and maintaining real-time data pipelines. You will work with our Product & Engineering team along the end-to-end algorithm lifecycle to advance the Trudenty Trust Network. A bit more on what you'll do: Data Engineering Develop and maintain real-time data pipelines for processing large-scale data Ensure data quality and integrity in all stages of the data lifecycle Develop and maintain ETL processes for data ingestion and processing Algorithm Development, Model Training and Optimisation Design, develop, and implement advanced machine learning algorithms for fraud prevention and user personalization Train and fine-tune machine learning models using relevant datasets to achieve optimal performance Implement strategies for continuous model improvement and optimization Data Mining & Analysis Apply data mining techniques such as clustering, classification, regression, and anomaly detection to discover patterns and trends in large datasets. Analyze and preprocess large datasets to extract meaningful insights and features for model training MLOps - Deployment into production environments, Monitoring and Maintenance Experience deploying and maintaining large-scale ML inference pipelines into production Implement and monitor model performance in production environments on Kubernetes and AWS cloud platforms. Utilize Docker for containerization and orchestrate containerized applications using Kubernetes. Code Review and Documentation Conduct code reviews to ensure high-quality, scalable, and maintainable code Create comprehensive documentation for developed algorithms and models Collaboration Collaborate with our cross-functional team; including the founders, sales, data scientists, engineers, and product to understand business requirements and implement effective solutions Research and Innovation Stay abreast of the latest advancements in fraud prevention and machine learning and contribute to the exploration and integration of innovative techniques About you: You will have proven experience with data science and a track record of implementing fraud prevention, credit scoring or personalization algorithms. Setting up and maintaining real data pipelines to feed your ML models is light work for you, and you would have been as comfortable if this JD was for a 'data engineer'. You have worked in a high growth and fast moving company. You are agile, comfortable with ambiguity and are a creative thinker who can apply research and past experiences to new problems. What we're looking for: Education & Experience: Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 5+ years of professional experience in a relevant area like fraud prevention or credit scoring Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like Apache Hadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale ML inference pipelines into production environments. Proficiency with Docker for containerization and Kubernetes for orchestration. Familiarity with AWS cloud platform (experience with GCP or Azure is a plus). Experience monitoring and optimizing model performance in production settings. Programming Languages: Strong coding skills in Python and SQL. Experience with Node.js, JavaScript (JS), and TypeScript (TS) is a plus. Statistical Knowledge: Solid understanding of statistical concepts and methodologies for analyzing and interpreting large datasets. Ability to apply statistical techniques to validate models and algorithms. Data Manipulation & Analysis: Proficient in data manipulation and analysis using tools like Pandas, NumPy, and Jupyter Notebooks. Experience with data visualization tools such as Matplotlib, Seaborn, or Tableau to communicate insights effectively. Our offer: Cash: Depends on experience Equity: Generous equity package, on a standard vesting schedule Impact & Exposure: Work at the leading edge of innovation building our machine-learning powered smart contracts for fraud prevention Growth: An opportunity to wear many hats, and grow into a role you can inform Hybrid work: Flexibility to work from home, with travel into London The process: Submit your CV along with answers to the handful of questions we ask of every candidate A 60min call to explore initial fit with the founders A 60min technical problem solving interview, alongside your potential ML colleague Final discussion with the Founder CEO to align before we make a formal offer

Our vision is to be the universal symbol of trust, bringing consumers and businesses together through reviews. We are well on our way-but there's still an exciting journey ahead. Join us at the heart of trust. As part of the MLOps team, you'll work closely with data scientists, software engineers, and other stakeholders to bring machine learning models to life-ensuring they're deployed, maintained, and monitored efficiently in production. You'll have the opportunity to improve model performance and infrastructure, all while contributing to Trustpilot's AI-based solutions. What you'll be doing: Model Deployment: Collaborate with data scientists to take machine learning models from development to production, ensuring quality work and scalability. Build Pipelines: Develop and maintain data and model pipelines, integrating seamlessly with our existing systems to support reliable, efficient workflows. CI/CD for ML: Design and implement continuous integration and delivery pipelines to streamline the deployment of machine learning models. Model Monitoring: Help monitor the performance of machine learning models post-deployment, ensuring reliability, scalability, and quality over time. Collaboration: Work with cross-functional teams to design solutions that meet business needs while adhering to best practices in machine learning and software engineering. Optimize: Continuously improve our infrastructure, ensuring we remain at the forefront of AI model production and delivery. Who you are: Solid technical foundation in both machine learning and software engineering. Experience deploying machine learning models in production environments. Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, scikit-learn). Familiarity with cloud platforms like GCP, AWS, or Azure. Experience with CI/CD pipelines for machine learning (e.g., Vertex AI). Experience with data processing frameworks and tools, particularly Apache Beam/Dataflow is highly desirable. Knowledge of monitoring and maintaining models in production. Proficiency in employing containerization tools, including Docker, to streamline the configuration of deployment settings. Problem-solving skills with the ability to troubleshoot model and pipeline issues. Good communication skills, enabling effective collaboration across teams. What's in it for you: A range of flexible working options to dedicate time to what matters to you Competitive compensation package + bonus 25 days holiday per year, increasing to 28 days after 2 years of employment Two (paid) volunteering days a year to spend your time giving back to the causes that matter to you and your community Rich learning and development opportunities supported through the Trustpilot Academy, LinkedIn Learning, and Blinkist Pension and life insurance Health cash plan, online GP, 24/7, Employee Assistance Plan Full access to Headspace, a popular mindfulness app to promote positive mental health Paid parental leave Season ticket loan and a cycle-to-work scheme Central office location complete with table tennis, a gaming corner, coffee bars and all the snacks and refreshments you can ask for Regular opportunities to connect and get to know your fellow Trusties, including company-wide celebrations and events, ERG activities, and team socials. Access to over 4,000 deals and discounts on things like travel, electronics, fashion, fitness, cinema discounts, and more. Independent financial advice and free standard professional mortgage broker advice Still not sure? We want to be a part of creating a more diverse, equitable, and inclusive world of work for all. We're excited to hear about your experiences as well as how you will contribute to our working culture. So, even if you don't feel you don't meet all the requirements, we'd still really like to hear from you! About us Trustpilot began in 2007 with a simple yet powerful idea that is more relevant today than ever - to be the universal symbol of trust, bringing consumers and businesses together through reviews. Trustpilot is open, independent, and impartial - we help consumers make the right choices and businesses to build trust, grow and improve. Today, we have more than 320 million reviews and 70 million monthly active users across the globe, with 140 billion annual Trustbox impressions, and the numbers keep growing. We have more than 1,000 employees and we're headquartered in Copenhagen, with operations in Amsterdam, Denver, Edinburgh, Hamburg, London, Melbourne, Milan and New York. We're driven by connection. It's at the heart of what we do. Our culture keeps things fresh it's built on the relationships we create. We talk, we laugh, we collaborate and we respect each other. We work across borders and cultures to be the universal symbol of trust in an ever-changing world. With vibrant office locations worldwide and over 50 nationalities, we're proud to be an equal opportunity workplace with diverse perspectives and ideas. Our purpose to help people and businesses help each other is a tall order, but we keep it real. We're a great bunch of humans, doing awesome stuff, without fuss or pretense. A successful Trustpilot future is driven by you we give you the autonomy to shape a career you can be proud of. If you're ready to grow, let's go. Join us at the heart of trust. Trustpilot is committed to creating an inclusive environment where people from all backgrounds can thrive and where different viewpoints and experiences are valued and respected. Trustpilot will consider all applications for employment without regard to race, ethnicity, national origin, religious beliefs, gender identity or expression, sexual orientation, neurodiversity, disability, age, parental or veteran status. Together, we are the heart of trust. Trustpilot is a global company and our data practices are designed to ensure that your personally identifiable information is appropriately protected. Please note that your personal information will be transferred, accessed, and stored globally as necessary for the uses and disclosures stated in our Privacy Policy. Apply for this job

MLOps Engineer European Remote $5,000 to $6,500 per month We are looking for a MLOps Engineer to join our DataOps team, a new and growing team within FXC Intelligence with a focus on being the intermediary between Data Platform and DevOps teams, supporting our AWS migration and working closely with the AI team. What you'll be working on: Building and maintaining our data infrastructure using DevOps and Data Engineering practices, prioritising the needs of stakeholders Collaborate with Data Practitioners across the company to gain an understanding of their pains and needs and support them where engineering or data science experience is required Help Data Scientists and ML Engineers write reliable code and ship it to clients Help Data Analysts and other people in the business by providing the necessary tools and processes Collaborate with the DevOps team regarding standards and best practices for working with infrastructure in the company Participate in the migration from on-prem to AWS in the area of data infrastructure Collaborate with the evolution of the data stack, focusing on scalability, reliability and transparency About the DataOps team: The DataOps team is a new and growing team within FXC, serving as a critical intermediary between the Data Platform team and DevOps, focusing on implementing core functionalities for databases and ETL/ELT tools The team plays a key role in the migration of infrastructure to AWS, ensuring efficiency and scalability DataOps also collaborates closely with the AI team to develop and maintain machine learning pipelines, supporting the deployment and management of AI models You should apply if you have: Experience with deploying, testing and monitoring ML models Experience with data orchestration/pipelines and data warehousing Good working knowledge of Python and data science libraries Operational familiarity with ML Infrastructure tools such as Kubeflow, MLFlow and neptune.ai An understanding of continuous integration and continuous deployment practices, as well as experience with tooling like GitHub actions and Gitlab CI These skills will help, but aren't essential: Familiarity with cloud Knowledge of Infrastructure as Code (Terraform, Terragrunt) Tech Stack: Clickhouse DBT Airflow Terraform, Terragrunt, Helm AWS Sagemaker Bedrock Gitlab CI DVC MLFlow/Kubeflow/Weights & Biases About us: FXC Intelligence is a leading provider of cross-border payments data and intelligence, providing some of the world's biggest companies, central banks and non-governmental organisations with the strategic insights, expertise and awareness to effectively compete in their chosen markets. By joining us, you will be diving into a world of data-driven exploration and innovation, revolutionising financial insights through cutting-edge technologies, machine learning and predictive analytics. Your contributions will shape the future of cross-border finance, helping clients to uncover better paths to growth and profitability, as well as being a trusted reference and source for many leading international publications. We are proud to produce industry-changing data and intelligence, aided by our company values of being customer-focused, taking ownership, knowledge, communication and leadership. We're an innovative company that strives to look after its team and we take pride in providing a positive company culture. Have a look at our careers page to see for yourself what it's like to work with us. Also, why not take a look at our employee engagement blog to see how our colleagues feel about working at FXC Intelligence! At FXC Intelligence, we believe in embracing diversity in all forms and fostering an inclusive environment. All applicants will be considered for employment without attention to ethnicity, religion, sexual orientation, gender identity, family or parental status, national origin, veteran, neurodiversity status or disability status.

Location: Central London - 1 day per week in the office We are currently recruiting a Machine Learning Engineer to join a global publishing company. Producing high-quality educational resources, they are publishing impactful research that has the power to improve society and empower policy makers. The Opportunity This is an opportunity to join an organisation with people who are passionate about data and its ability to empower and improve lives. Work with cross-functional teams to deliver production level ML/AI solutions. Implement MLOps with a focus on versioning and data security. Champion Machine Learning across the business. Mentor junior members of the team. Profit share bonus. Skills and Experience Python. MLOps. Strong communication skills. If you would like to be considered for the role and feel you would be an ideal fit with our team, then please send your CV to us by clicking on the Apply button below.

Our Shipamax Data Science team seeks a highly skilled and motivated Machine Learning Engineer with 2+ years' of experience. This team sits within Wisetech's Digital Document portfolio and develops AI-based software to extract information from documents, structuring critical logistics data that powers the seamless movement of goods worldwide. You would join a team of experienced data scientists and engineers passionate about solving complex real-world problems in a friendly and stimulating atmosphere. Job Overview As a Machine Learning Engineer, you will bring research into practice, developing new and improving existing machine learning models, expanding our existing product offerings. You will work closely with our R&D and Product teams to successfully integrate cutting-edge models into our commercial software, delivering exceptional data extraction capabilities to our valued customers. This role would give you the opportunity to work with state-of-the-art machine learning models, such as LLMs, using real-world datasets and leveraging abundant real-world unstructured data. You will also be involved in exciting projects like developing data extraction for new logistic document types. Responsibilities Collaborate with R&D and Product to bridge the gap between research and practical implementation. Develop new production-ready machine learning models and improve existing ones. Deliver impressive data extraction software that meets and exceeds customer expectations. Responsible for the quality and ongoing evaluation of our data sets on existing and new - ML models in pre-launch and in production. Requirements MSc in Computer Science, other STEM subjects, or comparable work experience. 2+ years of working experience in data science in a commercial environment. Good programming skills in Python. Experience in building and evaluating machine learning models. Experience with Natural Language Processing (NLP) techniques. Experience working with real-world datasets. Solid understanding of fundamental concepts of data science. Experience working with Numpy and PyTorch. Experience working with cloud environments is not essential, but it would be beneficial. Ideal Characteristics Excellent analytical skills and attention to detail. Hard-working, responsible. Loves learning and gets things done. Good communication skills working within a team. About WiseTech Global Our innovations and global technology enables, improves and empowers the world's supply chains. Having listed on the ASX in 2016, WiseTech Global is now an ASX 50, AU$10 billion+ company that is serious about expansion and technical innovation. Our mission is to change the world by creating breakthrough products that empower those that own, enable and operate the supply chains of the world. Before you apply From time to time, WiseTech Global may use external service providers to assist us with assessing applications, including background checks, on our behalf. Accordingly, by applying for this role and providing your personal information to WiseTech Global, you consent to WiseTech Global providing this information to our external service providers who are required to treat such information with strict confidentiality in line with privacy and data protection laws and regulations. We are a global team of passionate people enabling and empowering the supply chains of the world.

Optimove is a global marketing tech company, recognized as a Leader by Forrester and a Challenger by Gartner. We work with some of the world's most exciting brands, such as Sephora, Staples, and Entain, who love our thought-provoking combination of art and science. With a strong product, a proven business, and the DNA of a vibrant, fast-growing startup, we're on the cusp of our next growth spurt. It's the perfect time to join our team of 500 thinkers and doers across NYC, LDN, TLV, and other locations, where 2 of every 3 managers were promoted from within. Growing your career with Optimove is basically guaranteed. As a Machine Learning Engineer, you will be working within our Personalization team, helping to shape and drive the development of numerous products and initiatives that allow our customers to personalise messages across all digital touchpoints. This includes working with multi-modal data such as images, text, and more, leveraging cutting-edge technologies including Large Language Models (LLMs). This is an exciting opportunity at the forefront of machine learning, helping to bring Accessible Intelligence to our customers with great scope to make a key difference across both OptiX and Optimove's overall platforms. We are looking for an experienced Machine Learning Engineer to work on incredibly interesting projects as we take our personalization capabilities to the next level. You will focus on developing and advancing ML/AI across our platforms, researching and investigating new machine learning applications within the company, and improving pre-existing models. Role & Core Responsibilities Own the model development and release process across all products and internal platforms, including both OptiX and Optimove. Manage the cloud-hosted modelling environment. Operationalize models as APIs working in real-time and batch environments. Monitor production models, ensuring data quality and model performance. Develop predictive machine learning models for classification, ranking, and personalization purposes, utilizing multi-modal data including images and text. Leverage LLMs and other cutting-edge technologies to enhance product capabilities. Research and investigate new machine learning applications within the company, and improve on pre-existing models. Collaborate closely with product and development teams to define and prepare new ML applications. Analyse performance and continuously improve scoring processes for hosted models. Best Bits of the Job Exposure to a phenomenal array of machine learning domains, including massive-scale search, ranking, NLP, hybridization, classification, multi-modal data processing (images, text, etc.), and far beyond. Leveraging state-of-the-art technologies, including Large Language Models (LLMs), to enhance our products and services. Fully real-time architecture for data processing, model development, and deployment. Deploying and enhancing ML frameworks, optimizing for inference, and training/retraining cycles. Online testing for models with live data using proprietary A/B/N testing technology to rapidly determine what works (and what doesn't). A super-bright, supportive, and friendly machine learning team to work with in an environment where rapid experimentation is the norm. Regular time allocated to research new methods, build and test proofs-of-concept, and deploy to production instantly if effective. GPU support to efficiently train deep learning models. Minimum Requirements Minimum 3 years of experience in a similar role. Strong programming skills and a good understanding of software engineering principles and clean code practices. Expert-level knowledge of Python for machine learning and data manipulation (pandas, NumPy). Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization for various domains, including sports betting and gaming, where it might add value and what best practices look like. Full understanding of recommendation algorithms and their applications. Professional experience in personalization and/or predictive CRM, and micro-segmentation. Experience with CI/CD pipelines and Infrastructure as Code (IaC) tools (Terraform, Bicep, etc.).

Locus Robotics is a global leader in warehouse automation, delivering unmatched flexibility and unlimited throughput, and actionable intelligence to optimize operations. Powered by LocusONE, an AI-driven platform, our advanced autonomous mobile robots seamlessly integrate into existing warehouse environments to enhance efficiency, reduce costs, and scale operations with ease. Trusted by over 150 industry leading retail, healthcare, 3PL, and industrial brands in over 350 sites worldwide, Locus enables warehouse operators to achieve rapid ROI, minimize labor costs, and continuously improve productivity. Our industry-first Robots-as-a-Service (RaaS) model ensures ongoing innovation, scalability, and cost-effectiveness without the burden of significant capital investments. With proven capabilities in diverse workflows-from picking and replenishment to sorting and pack-out-Locus Robotics empowers businesses to meet peak demands and adapt to ever-changing operational needs. Are you a Machine Learning Engineer with a passion for reinforcement learning, multi-agent systems, and simulation at scale? We want to hear from you! At Locus Robotics, we're developing advanced simulation tools and ML systems to optimize the behavior of large autonomous fleets in dynamic environments. In this role, you will work on cutting-edge reinforcement learning (RL) models, multi-agent systems, and faster-than-real-time simulations to drive innovation in logistics, robotics, and beyond. You'll collaborate with a highly skilled team of engineers and data scientists to develop scalable ML models and deploy them into production environments using modern MLOps practices. If you're excited about solving real-world optimization problems, building high-performance ML infrastructure, and working with autonomous agent simulations, this is your opportunity to make a significant impact. This is a remote position based in England, Scotland, Portugal, Poland, or Spain. Candidates must be authorized to work in one of these countries without the need for work sponsorship. Responsibilities: Utilize, develop, and enhance simulation tooling and infrastructure to enable faster-than-real-time modelling of 1,000+ autonomous agents for various use cases such as fleet optimization, logistics, or robotics. Develop, deploy, and maintain machine learning models, with a strong focus on reinforcement learning (RL) and multi-agent systems to optimize fleet behavior in dynamic environments. Implement and improve MLOps pipelines to support continuous training, deployment, monitoring, and scaling of machine learning models in production. Collaborate with data engineers and software developers to ensure seamless integration of machine learning models with existing infrastructure and data pipelines. Stay up to date with advancements in reinforcement learning, distributed computing, and ML frameworks to drive innovation in the organization. Work with cloud-based solutions (AWS, GCP, or Azure) to deploy and manage machine learning workloads in a scalable manner. Qualifications: Master's degree or Ph.D. in Data Science, Computer Science, Mathematics, or a related field. 4+ years of hands-on experience designing and deploying machine learning models in production, with a focus on reinforcement learning (RL) and multi-agent systems (MAS). Advanced Python programming skills, with a strong emphasis on writing efficient, scalable, and maintainable code. Proven experience with TensorFlow/PyTorch/Jax, Scikit-learn, and MLOps workflows for training, deployment, and monitoring of ML models. Experience working with Polars and/or Pandas for high-performance data processing. Proficiency with cloud platforms (AWS, GCP, or Azure), including containerization and orchestration using Docker and Kubernetes. Hands-on experience with reinforcement learning frameworks such as OpenAI Gym or Stable-Baselines3. Practical knowledge of optimization algorithms and probabilistic modeling techniques (e.g., Bayesian methods, Gaussian Belief Propagation). Experience integrating models into real-time decision-making systems or multi-agent RL environments (MARL). Exposure to spatiotemporal data analysis, including time-series anomaly detection and forecasting. Familiarity with ROS (Robot Operating System) for robotics or simulation integration. Publications in top-tier conferences/journals (e.g., NeurIPS, ICML, ICRA, CVPR, ECCV, ICCV) are a plus. Proficient English written and verbal communication skills required to collaborate effectively with internal and external teams. Excellent analytical and problem-solving skills, with the ability to contribute effectively in a collaborative team environment.