274 jobs found

Requirements Proven experience operating at staff level (ownership of systems, not just pipelines) Experience building and scaling modern data platforms A track record of operating at staff or principal level: you've owned systems, shaped technical direction across teams, and influenced how engineering gets done - not just delivered pipelines Deep experience building and scaling production data platforms, including high-ingestion time series workloads, and strong hands on ability in Python and modern data stack components (orchestration, warehousing, observability) The ability to design for reliability and scale - you understand the trade offs in data system design and have made consequential architecture decisions you can speak to clearly A product mindset: you care about whether the data is actually useful and used, not just whether the pipeline ran green Experience with cloud data infrastructure (AWS or GCP) and a point of view on what good looks like The communication skills to lead without authority - influencing technical direction across teams and making the case for the right thing even when it's harder Strong programming skills in Python, with experience building production grade data systems Experience with modern data stack components (e.g.): Orchestration: Airflow / Dagster Warehousing: Snowflake / BigQuery / Redshift / ClickHouse Streaming (nice to have): Kafka / Flink Experience with cloud platforms (AWS / GCP) Experience with data observability and testing practices (Desirable) Experience in energy or climate tech (Desirable) Familiarity with time series data at scale (Desirable) Experience supporting ML pipelines in production (Desirable) Background in high growth startups or scale ups What the job involves We're looking for a Staff Data Engineer to lead the design and evolution of our data platform. This is a high impact, hands on role combining technical leadership, system architecture, and product thinking You'll work closely with engineering, data science, and energy domain experts to ensure that data is reliable, scalable, and directly drives business value You'll work across the data management service team alongside data and analytics engineers, and in close partnership with energy domain experts, data scientists, and the broader engineering organisation. This is a hands on senior technical leadership role - you'll be reviewing pull requests and setting architectural direction in the same week What makes this genuinely different: you're not inheriting someone else's vision of what a data platform should be. The cultural norms, the standards, the practices - these are yours to define. If you've wanted to build the right thing from the ground up, this is that opportunity Technical Leadership Shape the technical direction across batch and streaming pipelines, setting the architecture others build to Set standards for pipeline design and data quality Lead design reviews and mentor other data engineers Evaluate and introduce tooling where it raises the bar - and make the case for when it doesn't Data Platform & Pipelines Build and maintain robust ETL/ELT pipelines Build systems optimised for high ingestion, low latency querying of time series data (TSDS) Optimise pipelines for performance, cost, and reliability Enable self serve analytics and decision making across the company Reliability and observability Implement data quality frameworks with real teeth: SLAs, automated testing, lineage, and monitoring Establish practices specific to energy data: late arrivals, reprocessing, backfills, and the failure modes that matter in this domain Build the observability layer that makes the platform trustworthy without constant human oversight Scale and performance Identify and fix the bottlenecks that constrain us today Optimise pipelines for performance, cost, and reliability as data volumes grow Architect for the next order of magnitude, not just the next quarter Technical leadership and culture Set engineering standards for pipeline design, data quality, and system observability Lead design reviews and mentor data engineers, raising the bar for how the team works Act as a multiplier: the people around you should get better because of how you approach problems What Success Looks Like: What Success Looks Like The data platform handles tem's current scale without firefighting, and is architected for the next phase of growth Other teams can access, trust, and use data without routing requests through the data engineering team There is a tight, reliable feedback loop between data ingestion and consumption: trading, forecasting, and analytics teams make faster decisions because the data is there when they need it The data engineering team has clearer standards, better practices, and higher output than when you arrived

Kitman Labs is the performance intelligence company, disrupting and transforming the way the sports industry uses data to unlock the potential of the world's top athletes. Driven by a passion to innovate in the areas of sports performance, analytics and user experience, we have assembled a team of the industry's top data scientists, sports performance scientists, product specialists and engineers. Kitman Labs' advanced Intelligence Platform (iP) is now used by over 2000 teams in 50 leagues on 6 continents, including the NFL, Premier League, National Women's Soccer League and MLS. The Role Are you passionate about quality? Are you competent in executing test cases & passionate about SaaS products, applications & systems? We are looking for a Senior QA Tester to join our team to work with cross functional teams to ensure quality throughout our software development lifecycle, to help us assess & improve our software quality through manual and automated testing. As a QA Tester in Kitman Labs you will use your attention to detail and passion for quality to understand product requirements and develop comprehensive test plans that will deliver robust systems to our clients. We will expect you to Work directly with Engineering and Product to deliver high quality features to our clients Work cross functionally to participate in all aspects of the software development lifecycle from planning to execution of tests Execute test cases (manual or automated) and analyse results Evaluate product code according to specifications Identify test scenarios based on requirements Develop and agree test plans with product and engineering leadership Review test results and provide feedback to team leadership Identify software bugs and provide analysis to drive quality within the team Act as the voice of quality within the team at all times Must have skills Focused on quality with a strong attention to detail Knowledge of the SDLC Proven experience as a Quality Assurance Tester or similar role Experience with Agile testing methodologies. Proficiency in creating, executing, and managing test cases and test plans. Understanding of functional, regression, system, and user acceptance testing. Experience with defect tracking and management tools like JIRA, or equivalent. Strong ability to identify and report bugs, along with detailed documentation of issues. Nice to have skills Familiarity with Agile frameworks and Automation testing is a plus Self motivated with the ability to work on own initiative or as part of a team Communication and presentation skills ISTQB Foundation level certification or equivalent Experience with different test types Expertise using different test tools Knowledge of databases and SQL Analytical mind and problem solving aptitude Benefits Competitive salary Health insurance for employee & dependants Meaningful equity Pension Plan Life Cover Income protection Wellbeing benefits Location While this role allows for remote work, occasional face to face gatherings are recommended. Diversity In addition to building a team with diverse skill-sets, Kitman Labs is committed to hiring people with diverse backgrounds. We do not discriminate based on age, civil or family status, disability, ethnicity, gender, race, religion, or sexual orientation. If you are a person with a disability and require assistance during the application process, please let us know.

The Position As a machine learning engineer in AI Enablement, you will be working closely with folks that span across Computational Scientists, Research Scientists, AI/ML experts, Product leaders, DevOps, and everyone in between. You'll build, own, and constantly improve scalable AI/ML based systems that unlock the potential of our diverse scientific data, accelerating the discovery and development of life changing treatments for patients. Design, develop, and test robust, scalable, and maintainable AI/ML facing scientific web applications and backend systems. Build tools to evaluate AI/ML model performance and establish new ways to understand AI quality. Partner with product managers and scientists to understand user needs, shape requirements, and translate them into actionable technical specifications. Develop and maintain systems for collecting, structuring, and storing diverse scientific data that support advanced analytics, machine learning, and other data driven initiatives. Implement, adopt, or evaluate new AI/ML algorithms and analytical techniques. Contribute to architectural decisions, code reviews, and the evolution of our development processes. Be willing to span the stack and contribute where needed, even outside of your core area of expertise. Stay up to date with emerging technologies and industry best practices and adopt a culture of continuous learning, collaboration, and curiosity. Who You Are Master's or Ph.D. in Computer Science or similar technical field, or equivalent experience. Established professional experience in machine learning engineering roles. Strong proficiency with AI/ML frameworks, libraries, and toolsets. Expert knowledge of statistics, machine learning theory, and algorithms. Strong knowledge of ML performance optimization, GPU best practices. Experience with Kubernetes, relational databases, NoSQL databases, or data lakes, and experience working on cloud native architectures in public clouds (ideally AWS). Proven understanding and application of engineering best practices. Excellent communication skills and ability to build trusted partnerships with internal and external collaborators. Ability to quickly acquire new technologies and programming languages and a passion for continuous learning. Preferred But Not Required Experience with imaging or biological data and processes is a strong plus. Experience working with scientists or in a research environment is advantageous. Experience with workflow automation, GenAI, and/or agents is a plus. Additional Information This position requires onsite work 3 days per week. No relocation support. The statements herein are intended to describe the general nature and level of work being performed by employees, and are not to be construed as an exhaustive list of responsibilities, duties, and skills required of personnel so classified. Furthermore, they do not establish a contract for employment and are subject to change at the discretion of Roche Products Ltd. At Roche Products we believe diversity drives innovation and we are committed to building a diverse and flexible working environment. All qualified applicants will receive consideration for employment without regard to race, religion or belief, sex, gender reassignment, sexual orientation, marriage and civil partnership, pregnancy and maternity, disability or age. We recognise the importance of flexible working and will review all applicants' requests with care. At Roche difference is valued and we are proud to be an equal opportunity employer where you are encouraged to bring your whole self to work.

(INV) Senior Manager, AI & Data, Data Strategist, Infrastructure TC UKI Location: Manchester Other locations: Primary Location Only Salary: Competitive Date: 7 May 2026 Job description Requisition ID: At EY, we're all in to shape your future with confidence. We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world. AI and Data Strategist Grade: Senior Manager Manchester and London Position Overview We are seeking a Senior Manager grade AI and Data Strategist to guide and coordinate AI transformation initiatives for clients across the infrastructure sector. This role demands a strategic business technologist who can translate AI capabilities into tangible business value while designing the organisational frameworks needed to become an AI first enterprise. Your client impact Lead an engagement workstream consisting of AI consultants and architects to define comprehensive AI and data strategies tailored to client organisations across consumer products, retail, health, energy, and life sciences sectors Bring current global thinking and trends in the AI, Automation and Data space including within EY Carry out in-depth analysis of infrastructure specific value chain and identify a portfolio of high impact AI use cases, to drive revenue growth, operational efficiency, and competitive differentiation Assess organisational AI and data maturity and create tailored roadmaps for transformation including business cases, operating models, organisational structure and capabilities required to deliver the strategy Direct the definition of high level business and technology architectures and operating models that enable scalable AI deployment (encompassing both data and automation) Lead cross functional workshops and design sessions with senior stakeholder to influence and align diverse stakeholder groups around a unified AI vision Present complex AI concepts and recommendations to senior leadership audiences Prioritise AI initiatives based on strategic value, feasibility, and organisational readiness Contribute to solution development and oversee quality of proposals for AI led transformation Provide mentorship and guidance to team members and develop reusable assets Support change management initiatives to embed AI driven and automation enabled culture across client organisations. Ensure strategies align with ethical AI principles, regulatory compliance (e.g. GDPR, AI Act), and risk mitigation frameworks. Support internal processes to approve designs and proposals that have integrity and are commercially sound Essential Qualifications STEM degree or equivalent certification in Computer Science, Data Science, Engineering, or related field Essential Criteria Strong experience delivering enterprise level AI strategies for complex or multinational organisations Expertise and interest in the UK Infrastructure sector including HMG commitments such as National Infrastructure Strategy and Ten Point Plan for a Green Industrial Revolution Experience in designing operating models and organisational structures for data and AI functions Experience of working with client organisations to write business or investment cases Strong understanding of AI/ML technologies, including generative AI, predictive analytics, NLP, data and Intelligent Automation Knowledge of modern data governance frameworks and architectures, cloud platforms, and AI infrastructure including DAMA Knowledge of AI governance frameworks, model risk management, and regulatory requirements Strategic thinking with demonstrated ability to translate complex technical concepts into business language Soft Skills Team leadership and coaching skills Communication and presentation skills Stakeholder management skills, including facilitation and workshop design capabilities as well as the ability to influence senior stakeholders Demonstrated ability to collaborate in multidisciplinary teams (data scientists, engineers, business strategists) Continuous learning mindset to stay ahead of rapidly evolving AI landscape and strategies for deployment Strategic thinking with demonstrated ability to translate complex technical concepts into business language and explain how they drive business value Preferred Criteria Experience with Big 4 consulting methodologies and framework Preferred Qualification Certifications in relevant areas (e.g., cloud platforms, data governance, AI ethics, automation technologies)

Senior Data Scientist IIApplylocations: Farringdontime type: Full timeposted on: Posted 9 Days Agojob requisition id: R112250 Are you ready to take your data science expertise to the next level and lead impactful projects? Would you enjoy working on advanced machine learning models and cutting-edge analytics solutions? About our team: We are a fast-moving, high-impact Data Science & AI team building real-world GenAI and ML solutions across the entire LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering-everything from predictive models to enterprise GenAI apps to automation that transforms how teams operate.We are data science generalists who love variety. One day, it is designing a new GenAI workflow, the next it is deploying a model into Salesforce or engineering a pipeline in Databricks. We own our projects end-to-end and partner directly with stakeholders to deliver solutions that get used and make a measurable difference.If you want to experiment, build, ship, and see your work drive real impact across a global organisation, you will feel right at home with us. About the role: We are seeking a Senior Data Scientist II who is a Data Science Generalist. The ideal candidate is comfortable working across GenAI, traditional machine learning, analytics, data engineering, cloud platforms, and enterprise system integrations.In this role, you will design, build, and deploy AI and ML solutions that support key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering. You will work end-to-end across ideation, modelling, experimentation, prompt engineering, deployment, monitoring, and stakeholder communication.This position is ideal for a versatile data scientist who enjoys solving diverse problems, working with multiple systems, and driving measurable business impact. Responsibilities: Build GenAI applications using OpenAI APIs, embeddings, vector search, and retrieval-augmented generation (RAG). Design advanced prompt engineering patterns and automated evaluation frameworks for LLM quality and safety. Develop and deploy traditional ML models (e.g., churn, propensity, sentiment/feedback, lead scoring, customer intelligence). Own the end-to-end model lifecycle: data prep, experimentation, deployment, and monitoring. Build and optimize feature pipelines and scoring jobs using Python, Databricks, Spark, Delta Lake, and AWS. Use AWS services (S3, Redshift, Lambda) for data automation, orchestration, and scalable processing. Ensure data quality, observability, lineage, and documentation across data and ML pipelines. Deliver enterprise integrations with Salesforce (SFDC) and Oracle platforms (Fusion, Service Cloud, Peoplesoft) for batch and real-time workflows. Create analytics solutions with cross-functional partners: define KPIs, connect customer/product/finance/CRM data, and drive actionable recommendations. Productionise reliably: provide L2/L3 support, monitor drift/data quality/prompt performance, run root-cause analysis, and implement preventative fixes. Requirements: Strong Python programming skills. Direct experience with OpenAI APIs, LLM workflows, and prompt engineering. Solid machine learning fundamentals, including supervised learning, NLP, and feature engineering. Experience with Databricks, Spark, and Delta Lake. Strong SQL skills with experience working on large datasets. Experience with AWS, including S3 and Lambda. Familiarity with Redshift, Snowflake, or other cloud data warehouses. Experience with behavioral datasets. Ability to work across machine learning, data engineering, analytics, and integrations. Ability to design end-to-end solutions spanning data, models, APIs, and automation workflows. We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here . Please read our Candidate Privacy Policy.We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

Data Scientist IIIApplylocations: Farringdontime type: Full timeposted on: Posted 9 Days Agojob requisition id: R112246 Are you ready to grow your data science expertise and work on impactful AI and machine learning projects? Would you enjoy building advanced analytics, machine learning, and GenAI solutions that drive real business value? About our team We are a fast-moving, high-impact Data Science & AI team building real-world GenAI and ML solutions across the LexisNexis business. Our work powers smarter decisions for Product, Sales, Finance, Marketing, Customer Success, and Engineering-everything from predictive models to enterprise GenAI applications to automation that transforms how teams operate.We are data science generalists who enjoy variety. One day, it may be designing a new GenAI workflow, the next it may be deploying a model into Salesforce or developing a pipeline in Databricks. We work closely with stakeholders to build practical solutions that are used and deliver measurable impact.If you want to experiment, build, ship, and see your work make a difference across a global organisation, you will feel right at home with us. About the role We are seeking a Data Scientist III who is a strong Data Science Generalist. The ideal candidate is comfortable working across GenAI, traditional machine learning, analytics, data engineering, cloud platforms, and enterprise system integrations.In this role, you will help design, build, and deploy AI and ML solutions that support key business functions across Product, Sales, Finance, Marketing, Customer Success, and Engineering. You will contribute across the full solution lifecycle, including problem framing, data preparation, modelling, experimentation, prompt engineering, deployment, monitoring, and stakeholder communication.This position is ideal for a versatile data scientist who enjoys solving diverse problems, working across multiple systems, and contributing to measurable business impact. Responsibilities: Build GenAI applications using OpenAI APIs, embeddings, vector search, and RAG. Apply prompt engineering and help define evaluation approaches for GenAI outputs. Develop and deploy ML models (e.g., churn, propensity-to-buy, sentiment/feedback, lead scoring, customer intelligence). Own the full ML lifecycle: data prep, experimentation, deployment, and monitoring. Build and optimise feature pipelines and model scoring jobs with Python, Databricks, Spark, and Delta Lake. Use AWS (S3, Redshift, Lambda) for data automation and orchestration. Improve pipeline data quality, observability, lineage, and documentation. Integrate models/data with enterprise platforms (Salesforce, Oracle Fusion/Service Cloud/Peoplesoft). Deliver real-time and batch workflows to improve CRM, sales, service, and marketing operations. Partner cross-functionally to define KPIs, generate actionable insights, communicate clearly, and drive adoption via demos/docs/training. Requirements: Strong Python programming skills. Experience with OpenAI APIs, LLM workflows, and prompt engineering. Solid machine learning fundamentals, including supervised learning, NLP, and feature engineering. Experience with Databricks, Spark, and Delta Lake. Strong SQL skills with experience working on large datasets. Experience with AWS, including S3 and Lambda. Familiarity with Redshift, Snowflake, or other cloud data warehouses. Experience working with behavioural or business datasets. Ability to work across machine learning, analytics, data engineering, and integrations. Ability to contribute to end-to-end solutions spanning data, models, APIs, and automation workflows. We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location. We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Form or please contact 1-. Criminals may pose as recruiters asking for money or personal information. We never request money or banking details from job applicants. Learn more about spotting and avoiding scams here . Please read our Candidate Privacy Policy.We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

Join the Applied Artificial Intelligence and Machine Learning team as a Lead Software Engineer within Corporate and Investment Banking. You will play a pivotal role in transforming the operations of the world's largest bank. You will collaborate with Data Scientists and Line of Business teams to integrate AI/ML solutions and develop horizontal capabilities, focusing on creating robust APIs, services, and libraries. Job Responsibilities Develop and maintain high-quality applications using Python. Architect scalable and resilient cloud infrastructure solutions using AWS/Kubernetes/EKS/ECS. Design and deploy solutions with MLOps best practices. Collaborate with AI experts and internal teams to understand and integrate AI/ML with existing systems. Mentor and guide junior team members, lead initiatives to promote best practices and automation. Collaborate closely with SRE and production monitoring teams to ensure system reliability and performance. Required Qualifications, Capabilities and Skills Formal training or certification in Computer Science, Engineering, or a related field, along with strong advanced experience in key concepts. Proven hands-on experience with Python and Kubernetes or ECS. Experience with cloud platforms such as AWS. Ability to work independently to understand and integrate with other systems within a bank. Excellent communication and collaboration skills. Preferred Qualifications, Capabilities and Skills Practical understanding of MLOPS. Hands-on experience with Terraform.

Job Title Data Scientist, AI Agents Salary £60-100k + Equity Company Description Arctal - AI startup building high-fidelity financial datasets Location London, UK Job Description You will act as a super-IC managing a fleet of AI agents to transform 100,000+ unstructured financial PDFs into clean, queryable data for institutional buyers. This role sits at the intersection of data science and engineering, requiring you to design agentic workflows, prompt chains, and validation logic to ensure professional-grade accuracy at scale. Why this role is remarkable Work at the frontier of agentic workflows, using tools like Claude Code and Cursor to automate the extraction of complex data that was never meant to be machine-readable. Join an elite team of five with the output of fifty, founded by industry veterans from Secondmind and Sylvera with deep roots in Cambridge ML research. Enjoy high intensity growth and direct exposure to institutional customers like asset managers and banks, with meaningful equity in a fast shipping startup. What you will do Design and maintain agent led data pipelines using prompt chains and human in the loop checkpoints to ensure 100% data reliability. Build validation logic in Python and SQL that encodes your expert judgement to catch the edge cases and hallucinations AI agents might miss. Debug and optimize complex data ingestion and transformation systems, acting as the final line of defense before data reaches global financial clients. The ideal candidate Possesses 1-5 years of experience as a data scientist or analytics engineer with a track record of building production grade data pipelines, not just querying tables. Is an AI native practitioner who uses agentic coding tools like Cursor or Claude Code for real world engineering tasks rather than simple chat experiments. Demonstrates an obsessive attention to detail and technical depth in Python, SQL, and terminal environments, with a passion for defining "ground truth" in messy datasets.

Machine Learning Engineer Department: Project Delivery Employment Type: Permanent - Full Time Location: London Reporting To: Mike Winstanley Description The Machine Learning Engineer role sits within Client Delivery, embedded in the Data & Analytics Consulting (DACs) team - a technical, client facing group of Data Scientists and ML Engineers responsible for building and operationalising advanced machine learning and AI components across Xantura's projects. As an ML Engineer here, your core work is designing, training, evaluating, and productionising machine learning models on complex, multi source datasets from local authorities. You will engineer high performance training pipelines, build embedding based and sequence models, implement LLM and RAG workflows, and develop containerised model services that integrate directly into the OneView platform. This role is for engineers who want to build real models, ship real systems, and solve real operational ML problems - not just prototypes. You will work directly with production data, client technical teams, and our internal engineering ecosystem to deliver AI components that are robust, scalable, and deployed into live environments. Key Responsibilities Machine Learning engineering Design, train and optimise predictive models using advanced architectures such as gradient boosted trees, temporal models and embedding based models. Build robust training, evaluation and monitoring pipelines to ensure model quality, reproducibility and auditability. Implement feature engineering, hyperparameter tuning, model debugging and performance optimisation. Productionise models so they run reliably and efficiently at scale in client environments. Data engineering Own schema aware data flows for modelling and cohorts; validate, transform and version datasets used in training and inference. Manage and evolve database schemas; optimise SQL, indexing and partitioning for large training and scoring workloads. Technical delivery Lead the modelling and data engineering components of client projects alongside DACs and Business Consultants. Acquire and extract data from client source systems Build and validate cohort logic to ensure accuracy, interpretability and alignment with client needs. Troubleshoot and resolve complex modelling and pipeline issues throughout delivery. AI engineering Build and integrate LLM based components including embedding pipelines, RAG workflows and text analysis models. Develop and deploy agentic and multicomponent AI systems using modern ML frameworks. Engineer high performance NLP and sequence models for information extraction, classification and risk prediction. Engineering level platform configuration Configure advanced OneView components linked to modelling outputs such as risk logic, summaries and scoring pathways. Contribute modelling innovations, performance insights and engineering improvements back into the platform. Knowledge sharing and technical leadership Act as an SME for machine learning, AI and model engineering within DACs. Mentor DACs on Python, modelling best practice, data engineering fundamentals and debugging approaches. Produce documentation, templates and reusable components to raise engineering standards across delivery. What are we looking for? 3-5+ years' experience in machine learning engineering Strong Python engineering skills and experience with modern ML frameworks Practical experience training and evaluating models (tree based, temporal, embedding/NLP or LLM based) Ability to build reproducible training and evaluation pipelines Experience containerising and deploying models (e.g., Docker, Fast API) Solid data and database engineering Strong SQL and experience working with relational databases Understanding of schemas, data transformations and (ideally) DBT Experience preparing data for model training and scoring Hands on AI/LLM experience Working with embeddings, vector databases or RAG style workflows Experience applying NLP or sequence models to real world datasets Experience delivering technical work to clients or stakeholders Comfortable defining data requirements, discussing modelling decisions and troubleshooting issues in real time Clear communication and collaborative mindset Able to explain technical concepts simply and work closely with data scientists, engineers and consultants Bonus points if you have: Experience with Azure ML, AKS or similar cloud environments Experience with public sector datasets or analytical workflows Location This is a hybrid role based in our office in London (Borough). You would be expected to be able to work from the office at least 1-2 days per week. Some travel is also required for on site client engagements as needed. What can we offer you? Competitive salary reviewed annually Work for a passionate, mission driven company solving society's big problems Work flexible hours around life commitments with a focus on delivering company value rather than hours worked Ability to work remotely (excluding face to face Team Meetings and client meetings) Training and development opportunities 25 days annual leave (plus bank holidays) Company pension Private medical insurance Generous enhanced parental leave policies Cycle to work scheme Flu Vaccinations Eye Test and contribution towards Glasses for VDU use Employee Assistance Programme Mental health and wellbeing support Remote GP access Counselling/therapy Physiotherapy Medical second opinions

Senior Machine Learning Infrastructure Engineer London, United Kingdom About us PhysicsX is a deep-tech company with roots in numerical physics and Formula One, dedicated to accelerating hardware innovation at the speed of software. We are building an AI-driven simulation software stack for engineering and manufacturing across advanced industries. By enabling high-fidelity, multi-physics simulation through AI inference across the entire engineering lifecycle, PhysicsX unlocks new levels of optimization and automation in design, manufacturing, and operations - empowering engineers to push the boundaries of possibility. Our customers include leading innovators in Aerospace & Defense, Materials, Energy, Semiconductors, and Automotive. Note:We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals. The Role The Senior ML Infrastructure Engineer will extend and operate the infrastructure that powers our research model training, fine-tuning, and serving pipelines. You will be embedded within our Research function, partnering directly with ML engineers and research scientists to ensure they can train Large Physics Models efficiently and reliably at scale. Team Context In this role, you will be vertically embedded in Research, working daily with: Research Scientists who determine the model architectures and methods ML Engineers who implement and develop the models Simulation Data Engineers who are accountable for upstream data pipelines You will have end-to-end responsibilities over the research infrastructure, with the autonomy to make architectural decisions and the responsibility to keep data flowing reliably. Horizontally, you will be part of an infrastructure engineering group responsible for infrastructure across the company. What you will do Training Infrastructure Design and operate distributed training infrastructure for neural operator architectures (Transolver, Point Cloud Transformer, etc.) on our large NVIDIA DGX B200 platform. Optimize training pipelines for throughput, fault tolerance, and cost efficiency, including checkpointing strategies, gradient accumulation, and multi-node synchronization. Build and maintain experiment tracking and observability systems that give researchers clear visibility into training runs, hyperparameter sweeps, and model performance. Data I/O and Performance Solve data loading bottlenecks for large-scale mesh datasets. Optimize data pipelines for efficient I/O from cloud storage, including prefetching, caching, and format optimization. Work with heterogeneous data sources of varying formats and resolutions. Model Serving and Deployment Build serving infrastructure for pre-trained LPMs, supporting both zero shot inference and uncertainty quantification (Monte Carlo Dropout). Design and implement model packaging pipelines for customer deployment. Models must run reliably in customer environments with fine tuning capabilities. Ensure reproducibility: any model checkpoint should be deployable with consistent behaviour. Platform and Tooling Improve developer experience for the Research team with fast iteration cycles, reliable CI/CD, clear debugging tools. Collaborate with the broader Infrastructure team on shared patterns and standards. What you bring to the table Ability to scope and effectively deliver projects, prioritising activity as needed. Problem solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. Excellent collaboration and communication skills, especially in a research setting. You can translate "the model isn't converging" into infrastructure hypotheses and solutions, and can bridge technical abstractions with implementations. 5+ years of experience building and operating ML infrastructure at scale: Deep expertise in distributed training: you've debugged NCCL hangs, optimized collective communication, and know when to use FSDP vs. DDP vs. pipeline parallelism Strong systems fundamentals: Linux, networking (including domain specific NVLink and InfiniBand), storage I/O, profiling and performance optimization Production experience with Kubernetes and SLURM for job orchestration on GPU clusters Proficiency in Python and ML frameworks (PyTorch strongly preferred) Experience with cloud GPU infrastructure; ideally CoreWeave or similar GPU/HPC-focused clouds Ideally Experience with geometric deep learning or neural operators, architectures that operate on meshes, point clouds, or graphs Background in HPC for simulation engineering, familiarity with how CFD/FEA workflows generate and consume data Experience building model serving infrastructure with latency and throughput requirements Familiarity with experiment tracking tools (Weights & Biases, MLflow) and observability stacks (Prometheus, Grafana) What we offer Equity options - share in our success and growth. 10% employer pension contribution - invest in your future. Free office lunches - great food to fuel your workdays. Flexible working - balance your work and life in a way that works for you. Hybrid setup - enjoy our new Shoreditch office while keeping remote flexibility. Enhanced parental leave - support for life's biggest milestones. Private healthcare - comprehensive coverage Personal development - access learning and training to help you grow. Work from anywhere - extend your remote setup to enjoy the sun or reconnect with loved ones. We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with UK employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.

About Relation Relation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life's most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning to drive disease understanding-from cause to cure. This year, we embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. By combining our cutting edge ML capabilities with GSK's deep expertise in drug discovery, this partnership underscores our commitment to pioneering science and delivering impactful therapies to patients. We are rapidly scaling our technology and discovery teams, offering a unique opportunity to join one of the most innovative TechBio companies. Be part of our dynamic, interdisciplinary teams, collaborating closely to redefine the boundaries of possibility in drug discovery. Our state of the art wet and dry laboratories, located in the heart of London, provide an exceptional environment to foster interdisciplinarity and turn groundbreaking ideas into impactful therapies for patients. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. We cultivate innovation through collaboration, empowering every team member to do their best work and reach their highest potential. By joining Relation, you will become part of an exceptionally talented team with extraordinary leverage to advance the field of drug discovery. Your work will shape our culture, strategic direction, and, most importantly, impact patients' lives. The opportunity We are seeking a Principal Data Scientist, Computational Biology to join Relation, working at the intersection of single cell biology, spatial omics, and machine learning. In this role, you will apply advanced computational and statistical approaches to analyse high dimensional biological datasets, generating insights that directly inform disease understanding and drug discovery. You will sit within the Single Cell & Spatial Omics function, working closely with ML researchers, experimental scientists, and software engineers to translate complex biological data into actionable knowledge. This is a highly collaborative, scientifically driven role, suited to someone who enjoys working deeply with data, challenging models with biological insight, and contributing meaningfully to interdisciplinary research programmes. Your responsibilities Analyse and interpret single cell, spatial, and other multi omics datasets to uncover biological mechanisms relevant to disease and therapeutic intervention. Develop and apply statistical and computational methods for transcriptomics and related omics data. Use domain expertise to design rigorous evaluation tasks that test, challenge, and refine ML models. Collaborate closely with ML scientists to inform model assumptions, features, and interpretation. Work with experimental teams to help design experiments and validate computational hypotheses. Clearly communicate insights, results, and methodologies to internal stakeholders and contribute to scientific publications. Professionally, you have A PhD in computational biology, bioinformatics, statistics, physics, mathematics, or a related quantitative field. Strong experience working with high dimensional biological data, including transcriptomics and other omics modalities. Proficiency in Python, with experience in scientific computing and data analysis. A solid understanding of statistical modelling and quantitative methods applied to biological data. Experience with Bayesian models. Desirable knowledge or experiences Hands on experience with single cell and/or spatial omics data, including patient derived samples. Familiarity with machine learning approaches used in biological or biomedical research. Experience working in interdisciplinary teams spanning biology, data science, and ML. Exposure to scalable computing environments. Personally, you are A collaborative, inclusive team player who thrives in interdisciplinary environments. A clear and thoughtful communicator, comfortable explaining complex ideas across disciplines. Curious, impact driven, and motivated by scientific discovery. Humble, open minded, and eager to learn. Passionate about applying data and science to improve patients' lives. Join us in this exciting role where your contributions will have a direct impact on advancing our understanding of genetics and disease risk, supporting our mission to get transformative medicines to patients. Together, we're not just doing research; we're setting new standards in the field of machine learning and genetics. The patient is waiting! Relation Therapeutics is a committed equal opportunities employer. RECRUITMENT AGENCIES: Please note that Relation Therapeutics does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.

About Relation Relation is an end-to-end biotech company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life's most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning to drive disease understanding-from cause to cure. This year, we embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. By combining our cutting-edge ML capabilities with GSK's deep expertise in drug discovery, this partnership underscores our commitment to pioneering science and delivering impactful therapies to patients. We are rapidly scaling our technology and discovery teams, offering a unique opportunity to join one of the most innovative TechBio companies. Be part of our dynamic, interdisciplinary teams, collaborating closely to redefine the boundaries of possibility in drug discovery. Our state-of-the-art wet and dry laboratories, located in the heart of London, provide an exceptional environment to foster interdisciplinarity and turn groundbreaking ideas into impactful therapies for patients. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. We cultivate innovation through collaboration, empowering every team member to do their best work and reach their highest potential. By joining Relation, you will become part of an exceptionally talented team with extraordinary leverage to advance the field of drug discovery. Your work will shape our culture, strategic direction, and, most importantly, impact patients' lives. The Opportunity Join the innovative Turing team, where we build computational systems that help scientists explore biological data, generate hypotheses, and reason about mechanisms of disease. As a Senior / Principal Data Scientist, you will develop and apply modern AI approaches, including large language models, agentic workflows, and data-driven evaluation systems, to accelerate scientific discovery. The Turing team sits at the intersection of machine learning, data science, and biology. We build systems that integrate diverse biomedical datasets, extract insights from the scientific literature, and support decision-making in target discovery and therapeutic development. Our work combines strong engineering, machine learning research, and deep engagement with disease biology. This role is ideal for someone excited about applying LLMs and agent-based systems to complex scientific problems, working closely with biologists, data scientists, and engineers to turn computational ideas into practical tools for discovery. Your Responsibilities Develop and deploy LLM-driven systems to support scientific discovery, including literature analysis, hypothesis generation, and knowledge extraction. Design and implement agentic workflows that integrate multiple tools, models, and data sources to support biological reasoning and research workflows. Work with large, heterogeneous biomedical datasets (omics, literature, knowledge bases, and clinical data) to generate insights relevant to target discovery. Develop evaluation frameworks and benchmarks to assess the performance and reliability of AI systems in scientific contexts. Collaborate closely with biologists, computational scientists, and engineers to translate biological questions into computational solutions. Prototype and productionise data pipelines, models, and analysis workflows that support ongoing discovery programmes. Contribute to the development of new computational approaches for reasoning over biological knowledge and evidence. Professionally, You Have A PhD or equivalent experience in computational biology, machine learning, data science, or a related quantitative field. Strong practical experience working with large language models and modern AI tooling, including prompt engineering, tool use, retrieval systems, or agent-based architectures. Strong programming skills in Python, with experience working with large-scale datasets and modern ML tooling. Experience designing evaluation frameworks, benchmarks, or testing methodologies for machine learning systems. An understanding of biological data and the drug discovery process, or a strong motivation to work in this domain. Desirable Knowledge or Experience Experience applying LLMs or AI systems to scientific or biomedical data. Familiarity with literature mining, knowledge integration, or scientific knowledge representation. Experience building multi-step or agent-based reasoning systems. A background in computational biology, genomics, or drug discovery. Experience working in cross-disciplinary teams combining machine learning and biology. Personally, You Are A collaborative team player who enjoys working in interdisciplinary environments. A clear communicator who can translate complex ideas across computational and biological teams. Curious, pragmatic, and motivated by solving meaningful scientific problems. Excited to work on technology that can have real impact on patients. Humble, thoughtful, and eager to learn. Join us in this exciting role where your contributions will have a direct impact on advancing our understanding of genetics and disease risk, supporting our mission to get transformative medicines to patients. Together, we're not just doing research; we're setting new standards in the field of machine learning and genetics. The patient is waiting! Relation Therapeutics is a committed equal opportunities employer. RECRUITMENT AGENCIES: Please note that Relation Therapeutics does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.