76 to 100 of 140 Feature Engineering Jobs in London

multi-year observability, cost, and reliability investments that move WRITER's enterprise customers. Cross-functional collaboration. Operate at the seams with product, security, and engineering peers — provide expert guidance on system design for reliability, performance, and scalability from conception through launch, Connect the infra agenda to product and revenue … context, and disagree with evidence, not volume. What you need Technical Track record. 5+ years of experience in infrastructure engineering, DevOps, or a similar role focused on building and operating large-scale, high-availability production systems at a high-growth product company. Breadth. Experience running containerisation in production ...

Barcelona, Milan, Edinburgh and Madrid. Now is a brilliant time to join us and help shape the future of travel. Introducing the Embedded Data Engineering in ML Team At the heart of our Data and ML teams, embedded Data Engineers create the pipelines and tables that power business critical … Machine Learning Engineers and Data Scientists to build reliable datasets for ML use cases. You'll also have access to Trainline's wider Data Engineering, Data Platform, and analytics community, working alongside other embedded Data Engineers in ML, including senior and principal engineers. In this role as the Embedded ...

scales effectively, directly influencing the way millions of people engage with their products every day. Opportunity This is a unique chance to combine data engineering with machine learning in a high‐impact environment. You’ll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable … user experiences for a vast audience. Day‐to‐day Your day‐to‐day will include: Building and maintaining end‐to‐end data pipelines and feature engineering workflows. Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML. Driving best practices ...

evaluate, and improve machine learning models and algorithms for Content AI applications. Design and implement end-to-end machine learning solutions, including data preparation, feature engineering, model training, validation, deployment, monitoring, and continuous optimisation. Apply statistical analysis, experimentation, and data-driven decision-making to assess model performance … reliability, latency, drift, and business impact. Evaluate trade-offs between model quality, scalability, latency, and infrastructure cost when developing AI-powered products. Partner with engineering teams to deploy and scale machine learning systems in production. Produce clear technical documentation and share expertise through mentorship, design reviews, and technical leadership. ...

have missed. Develop and evaluate statistical and machine learning models. Build segmentation, propensity, forecasting and personalisation solutions that reach real customers. Apply rigour to feature engineering, validation and model performance. Design and evaluate experiments to understand genuine impact. Work with Data Engineering to create reliable model inputs ...

Data Science team is building a layered reliability platform that shifts CoreWeave from reactive troubleshooting to proactive reliability engineering. The platform spans telemetry ingestion, feature engineering, anomaly detection, failure prediction, distributed straggler detection, and agentic root cause analysis. As a forward deployed function, we partner closely with Fleet … intersection of data science and production systems, deploying advanced statistical models and machine learning methodologies directly within operational environments. You will collaborate closely with engineering and infrastructure teams to optimize GPU utilization, workload scheduling, and system efficiency in real time. You will design experiments, analyze large-scale system telemetry ...

machine learning platforms. Data governance and metadata tooling to improve transparency, trust, and reuse across the organisation. Your Experience 5+ years experience in data engineering, building and maintaining scalable data platforms. Strong SQL and data modelling skills, with experience designing analytical datasets. Experience with modern data stack tools (e.g. … e.g. Cube.js) or metrics‐layer design. Exposure to data governance and metadata tools (e.g. OpenMetadata, Hive Metastore). Experience supporting machine learning workflows and feature engineering pipelines. Why This Role Matters This platform is central to how we enable trusted, scalable, and reusable data across sportsbook. It provides ...

Development: Design, train, and optimise machine learning models focused on user personalisation, encompassing recommendation engines, ranking algorithms, user segmentation, and content analysis Data Pipeline Engineering: Construct and maintain robust and scalable data pipelines for feature engineering and model training utilising both structured and unstructured large-scale datasets ...

production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment. Key Responsibilities of the ML Engineer: Build … automate end-to-end ML pipelines covering feature engineering, model training, scoring and deployment Productionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready code Develop scalable ML solutions using Python, PySpark, Databricks and MLflow Deploy machine learning models into batch ...

deliver modern Machine Learning solutions that improve recommendations, matching and personalisation across Bumble. Own defined ML problems end-to-end, from data exploration and feature engineering through model training, evaluation, production deployment and iteration. Use modern ML frameworks such as PyTorch or TensorFlow to design, train and optimise … production models, diagnosing issues and improving reliability and performance at scale. Write high-quality, maintainable code and contribute to code reviews, technical discussions and engineering best practices. Take ownership of delivering high-quality solutions from insight through to measurable impact, balancing speed, quality and technical rigour. Apply responsible ...

Bring Hands‐on experience in data science or applied machine learning. Strong understanding of Generative AI, LLMs, and RAG principles. Experience with data mapping, feature engineering, and end‐to‐end ML lifecycle management. Proficiency across Microsoft's Azure AI and data ecosystem, with an eagerness to adopt Fabric ...

financial services (ideally Anti-Financial Crime/Screening) Experience building Natural Language Processing (NLP)/Machine Learning (ML)/Artificial Intelligence (AI) pipelines, including feature engineering, model training, evaluation and monitoring Experience with hyperparameter tuning at scale and experimentation frameworks Strong technical experience with SQL and Python; significant ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands‐on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

forecasting, behavioural signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready. Own the full model lifecycle from problem framing, data exploration, feature engineering, back-testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production. Partner closely with engineering, data … Strong hands-on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams. Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business ...

Responsibilities: Develop and implement machine learning and AI models Develop test and evaluation protocols on ML/AI models Assist in data pre-processing, feature engineering, and exploratory data analysis Contribute to building scalable and maintainable AI software systems Debug, test, and improve existing models and codebases Document … soon as practical. About Us The National Physical Laboratory (NPL) is a world-leading centre of excellence that provides cutting-edge measurement science, engineering and technology to underpin prosperity and quality of life in the UK. NPL and DSIT have strong commitments to diversity and equality of opportunity ...

production-grade ML solutions, working closely with Data Scientists to take models from development through to reliable production environments. This is a hands-on engineering role focused on ML pipelines, productionisation, deployment and ongoing model lifecycle management within a modern Databricks environment. Key Responsibilities of the ML Engineer: Build … automate end-to-end ML pipelines covering feature engineering, model training, scoring and deploymentProductionise models developed by Data Scientists, transforming notebooks and prototypes into modular, tested and production-ready codeDevelop scalable ML solutions using Python, PySpark, Databricks and MLflowDeploy machine learning models into batch and real-time environments ...

Solid Python programming skills and understanding of machine learning fundamentals. Growing familiarity with AI technologies, particularly LLMs and Azure OpenAI. Experience with data preparation, feature engineering, and model evaluation. Good communication skills and ability to explain technical concepts to non-technical audiences. A commitment to Movar's IDEAL ...

economic return optimization. You’ll develop calibration frameworks, ensure compliance with regulatory and fairness standards, and explore novel methodologies — including LLMs for explainability and feature engineering. Collaborating with cross-functional teams, you’ll translate modeling insights into strategic credit policies and business value, while mentoring junior team members ...

passionate about building scalable software that helps organisations detect fraud, verify identity, and make better decisions using advanced analytics? Do you enjoy collaborating across engineering, data science, and product teams to turn intelligent solutions into reliable products that deliver real-world customer value? About the BusinessLexisNexis Risk Solutions … Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the RoleYou will join an engineering team building software for fraud and identity analytics. In this role, you will design, build, test, and operate scalable software products, working closely with ...

passionate about building scalable software that helps organisations detect fraud, verify identity, and make better decisions using advanced analytics? Do you enjoy collaborating across engineering, data science, and product teams to turn intelligent solutions into reliable products that deliver real-world customer value? About the Business LexisNexis Risk Solutions … Customer Data Management. You can learn more about LexisNexis Risk at https://risk.lexisnexis.com/About the Role You will join an engineering team building software for fraud and identity analytics. In this role, you will design, build, test, and operate scalable software products, working closely with data ...

move more freely between more people , making the online shopping experience seamless and accessible to everyone. THE TEAM ~110 people , more than half in engineering, product and data 45+ advanced degrees across computer science, mathematics and operations research Thousands of data points captured, calculated, analysed and predicted for every … Work with team members across the business to understand their data needs and deliver solutions Shape our practices — Help define and evolve our analytics engineering processes and standards as we scale What We're Looking For 5+ years of experience as an analytics engineer, data engineer, data analyst ...

expand their machine learning skills. Responsibilities Create statistical summaries to support hypothesis testing and data-driven decisions during EDA Implement data preparation and feature engineering pipelines for the models Plan and implement algorithms for predictive modeling. Provide continuous improvement of models in production Lead the ML-driven python ...

BigQuery to build and support high-performance models at scale. What you'll do Contribute to the full model lifecycle , from initial discovery and feature engineering through to production, experimentation, and continuous monitoring. Design and refine real-time ML systems that reduce false positives and ensure a smooth ...

Data Scientist to drive production‐ready ML solutions that impact business outcomes. You will translate complex data challenges into scalable models, spanning data extraction, feature engineering, experimentation, and deployment. You will work closely with Product, Engineering and Marketing to optimize campaigns, improve user experiences, and inform strategic … decisions, while maintaining engineering rigor and robust ML ops practices. #J-18808-Ljbffr ...

Monitoring (TM)solution, you will own the tactical execution of features in an enterprise-grade AML/CLMplatform.You’llwork closely with cross-functional teams (engineering,customers, productmanagersandclient solutions) to deliver mission-critical capabilities:detectionengines,AI models,AI agents,tuning & calibration,alertand case managementUX, audit and reporting. You will translate … evolving threatsusing ground-breaking technology. Key Responsibilities Decompose high-level features/requirementsinto user stories, acceptance criteria, and clear backlog prioritization. Collaborate with engineering, data science, andsolutions teamsto deliver high-quality TM features (real-time and batch pipelines,detection analytics,alert scoring,alert/case creation, feedback loops). ...