Top Machine Learning Jobs in London

Design and validate agentic AI capabilities for Protege in PatentSight using ML, NLP, and LLMs. Build retrieval and hybrid search strategies, run experiments, evaluate models, analyze large IP datasets, and translate results into product recommendations while collaborating with engineers, product, and IP experts.

Own product messaging, positioning, and competitive intelligence for PolyAI voice assistants. Define go-to-market strategy, develop personas and sales enablement collateral, drive demand and analyst relations, run customer research, maintain competitive databases, scale the product marketing team, and represent the company externally.

Lead development and implementation of analytics and predictive models to support R&D Quality oversight across clinical, safety, laboratory, and vendor data. Build dashboards, risk indicators, automated monitoring, and audit-support tools; integrate complex data to identify portfolio-level risks and translate findings into actionable quality recommendations that support GCP/GVP/GLP/GCLP compliance and inspection readiness.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

The Graduate Machine Learning Researcher will design, test, and implement machine learning models to improve predictive accuracy in sports betting analytics. In a collaborative environment, they will interface with experienced professionals and be involved in R&D from design to implementation, utilizing their mathematical insights.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Design and evaluate memory architectures for foundation models across modalities. Build production-ready, scalable learning models and algorithms, define memory evaluation metrics, collaborate with researchers and engineers, and guide roadmap decisions for long-horizon model behavior and efficient memory storage/retrieval.

The Senior Machine Learning Engineer will develop and deploy ML models, enhance infrastructure, and collaborate on customer projects, contributing to innovative solutions for carbon emission reduction.

Lead design and delivery of production-grade AI agent services: build LangChain-driven LLM workflows, design distributed microservices, implement testing/evals and monitoring, deploy and maintain models, guide architecture decisions, write production-quality code, troubleshoot live systems, produce documentation, and mentor junior engineers.

Lead ML engineering role bridging deep model research and production. Own end-to-end ML systems, translate research findings into engineering work, turn production regressions into research probes, and set architectural direction. Collaborate with Data & Analytics and Global Delivery, mentor others, and build evaluation, federation, and serving infrastructure with technical autonomy.

10-week summer ML research internship based in Central London. Work on a supervised research project using advanced ML methods, mentored by senior researchers, with access to large compute, culminating in a final presentation. Top performers considered for full-time roles.

Build and maintain scalable MLOps infrastructure and CI/CD for ML and generative AI. Implement experiment tracking, model versioning, deployment, monitoring, IaC, containerized workloads, and production lifecycle management. Collaborate with data scientists and engineers to productionize, monitor, retrain, and govern models in cloud-native environments.

Build, fine-tune, and operationalize ML models and end-to-end AI applications; collaborate with GTM and partners to deliver PoCs and customer deployments; contribute features and fixes to the ML platform; enable new models and optimize inference performance and scalability.

Lead the development of AI/ML infrastructure for simulations using large foundation models. Collaborate on realistic simulation improvements and mentor junior engineers.

Manage requests for new ServiceNow reports and dashboards, resolve dashboard/reporting incidents, train users on dashboards, build executive KPIs and scorecards, engage stakeholders to gather requirements, and apply AI/Generative AI for reporting automation and insights.

Build, deploy and maintain production-grade machine learning systems for personalisation and product discovery. Develop recommender and ranking models, deploy to batch and real-time environments, monitor performance, collaborate with data scientists and engineers, and contribute to MLOps tooling and platform practices to improve model reliability and scalability.

Design, build and operate large-scale recommendation, ranking and personalization ML systems (batch and real-time). Collaborate with scientists and engineers to deploy production deep learning models, improve system performance, mentor engineers, and shape ML best practices and MLOps across the team.

Design, build, deploy and operate large-scale production ML systems for personalised outfit recommendations and AI-driven fashion experiences. Collaborate with ML scientists and engineers to deploy deep learning and generative models, optimise performance and scalability, improve reliability and observability, and mentor other engineers while contributing to shared MLOps capabilities.

Lead identification, development, and global deployment of AI and automation solutions to optimize forms and precedents content operations. Prioritize opportunities, collaborate with cross-functional teams, ensure governance and quality, integrate solutions, report outcomes to leadership, and drive continuous improvement and responsible AI practices.

As a Lead Data Scientist, you will set technical direction, ensure project delivery, manage client relationships, and mentor data scientists.

The Computer Vision Engineer will lead AI initiatives in the defense sector, delivering solutions and mentoring teams while driving technical strategy and capability development.

The role involves building and operating ML systems and infrastructure for large foundation models, data pipelines, and experiment orchestration in a collaborative environment.

Deploy and optimize LLM-based solutions for real-world government, healthcare, and energy problems. Build scalable data pipelines, productionize models (RAG/agents), monitor performance, engage stakeholders including government officials, and collaborate with cross-functional teams to deliver impactful AI products.

Own end-to-end productionisation of ML models, build and mature the ML platform and serving architecture, integrate system testing and monitoring into training pipelines, automate releases and drift detection, and measure real-world impact alongside data scientists.