Top Machine Learning Jobs in London

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution environments.

As Lead Data Scientist, oversee project teams delivering AI solutions, mentor junior team members, and shape technical strategy for financial services clients.

As a Machine Learning Engineer, you will develop software solutions using machine learning, collaborate with clients, and deliver impactful projects working within cross-functional teams.

As the Technical Director of AI Safety, you'll lead AI safety initiatives, oversee R&D, mentor teams, and drive innovations, representing Faculty in global AI safety discussions.

Design and ship machine learning models for optimizing messaging on multiple channels, focusing on long-term user outcomes and experimentation.

Develop and maintain quantitative equity models, alpha signals, factor research, and thematic investment insights for institutional investors. Produce client-facing research, conduct bespoke portfolio and market analyses, and enhance scalable research infrastructure using machine learning, agentic AI, automation, and advanced analytics. Translate complex quantitative findings into clear investment narratives and support client engagement.

Leads multi-quarter information security roadmaps and complex security initiatives across cloud security, IAM, Secure SDLC, regulated data transfers, and agentic AI workflows. Partners with engineering, machine learning, research, and business teams to deliver security programs, manage risk and dependencies, advocate for InfoSec capabilities, and provide CISO-level visibility through automated dashboards and operational metrics.

Provides scientific leadership translating foundation models into drug discovery and translational research workflows. Leads model evaluation and applied biology use cases, mentors junior researchers, partners with Product and Business Development, and converts biology and machine learning research into customer deliverables. Requires expertise in computational biology, machine learning for biological data, modern ML frameworks, early drug discovery, scientific communication, and roadmap leadership.

Build and post-process CFD models, including geometry cleanup, meshing, simulation, and results analysis. Support parametric CAD, workflow automation, design optimization, and simulation dataset generation for machine learning models. Collaborate with data scientists, machine learning engineers, and customers, using cloud and HPC resources. Prepare customer presentations, apply engineering best practices, and travel occasionally for customer engagements.

Lead software product strategy, roadmap development, requirements definition, product launches, stakeholder engagement, OKRs, performance measurement, and cross-functional collaboration with engineering and business teams. The role requires translating business needs into technical product solutions, prioritizing features, documenting requirements, monitoring adoption and feedback, and driving data-informed decisions across multiple business-critical projects.

Design, build, and productionise end-to-end ML and GenAI systems (retrieval, ranking, LLM conversational components) for personalised product recommendations. Develop evaluation frameworks for LLM outputs, integrate monitoring and experimentation pipelines, and partner cross-functionally to deliver production-ready solutions and best practices.

Partner with account executives and architects to win digital-native customers across the UK and Ireland. Lead hands-on evaluations, proofs of concept, technical demonstrations, and competitive positioning for Databricks’ data and AI platform. Engage engineers, founders, and executives; communicate technical and commercial value; develop customer-facing collateral; and support workshops and technical community events. The role requires regular London office attendance and approximately 20–30% travel across the region.

Develop and maintain data science models for marketing measurement products, including attribution, forecasting, optimization, and experimentation. Own analytical investigations, model backtesting, production bug fixes, and pipeline work. Write and review Python and SQL production code, debug unfamiliar repositories, use AWS and QA tooling, and communicate findings to clients and colleagues. The role also involves applying machine learning methods, reviewing AI-assisted code, clarifying requirements, and progressively taking ownership of technical areas.

Manage a 3–6 engineer Search Retrieval team, delivering search features and experiments that improve purchasing metrics. Own team wellbeing, performance, career growth, roadmap execution, and engineering standards. Collaborate with Product, Design, Marketing, Analytics, Machine Learning, and Data Science partners while contributing strategically and remaining technically engaged across front-end and back-end development. Foster inclusive, high-performing team culture and make data-informed decisions amid changing priorities.

Lead and manage a team of senior machine learning engineers developing production offline scene-understanding and counterfactual evaluation models for autonomous driving. Own technical direction, architecture, roadmap, hiring, engineering quality, and cross-functional alignment across UK and US teams. Apply computer vision, foundation models, large-scale training, and offline compute to improve validation, rare-event mining, simulation, and customer-facing deliverables.

Lead ML Engineer to build ML experimentation tooling and label quality for Fincrime/KYC/Support squads. Own roadmap, stakeholder management, delivery, and onboarding. Work across MLOps, distributed data processing, Terraform/AWS infra, CI/CD, monitoring, and ML governance; mentor junior team members and run PoCs to production.

Designs and delivers enterprise AI solutions using Claude. Acts as a pre-sales technical advisor, translating customer needs into scalable architectures, guiding evaluations and deployments, presenting to technical and executive audiences, and coordinating with Sales, Product, and Engineering. The role requires customer-facing architecture experience, enterprise integration expertise, Python familiarity, LLM knowledge, German and English fluency, and occasional customer travel.

The role involves defining and pursuing a research project in general-purpose world models, collaborating with teams to advance visual AI.

Own multi-year product vision and roadmap for a UK home-services marketplace. Lead and grow PM teams, prioritise commercial and platform investments, integrate AI/ML across product (search, matching, personalisation, automation), partner cross-functionally, and represent product to the board and investors to drive revenue, retention, and growth.

Lead product strategy and execution for core finance product areas, turning complex financial workflows into intuitive AI-powered experiences. Partner with Engineering, Design, Data, and GTM teams; work directly with CFOs and finance teams to discover user needs, define priorities and metrics, and ship high-impact features from 0→1 to scale.

Lead statistical strategy and delivery for Phase I-IV clinical trials embedded with a pharmaceutical sponsor. Design analyses, support protocol and reporting activities, oversee programming/CRO work, apply SAS/R (R Shiny)/Python and ML/DL methods for analyses and automation, and mentor junior statisticians.

The Senior Product Manager will drive product initiatives for UK Cards, improving customer experience and leveraging AI tools while collaborating cross-functionally on growth strategies.

The Product Manager will enhance the UK Cards product by driving growth initiatives and AI-enabled workflows, collaborating across teams and improving customer experience.

Design and document market-data workflows, integrations, and data pipelines for risk analytics. Translate business needs into specifications, data mappings, user stories, and test plans. Investigate data issues using SQL/Python, support UAT and releases, and improve controls, monitoring, automation, and operational processes across Market Data and Integration.

Manage and scale systematic trading strategies for global agricultural commodities by combining fundamental research with quantitative modeling, alternative datasets (weather, satellite, logistics), rigorous backtesting, portfolio construction, risk management, and productionalizing research into live trading systems.