Top Machine Learning Jobs in London

Lead applied AI/ML efforts to design, build, deploy, and maintain production ML services (NLP/CV focus). Collaborate with engineering, cloud, and SRE teams, research models, ensure MLOps best practices, and document processes for regulatory compliance.

Design, build, and deploy scalable ML services combining NLP, CV, and statistical methods. Collaborate with engineers, cloud and SRE teams to productionize models, ensure observability and compliance, and maintain models over time. Communicate results to stakeholders and document processes for regulatory needs.

Investigate autonomous vehicle issues across hardware, software, models, and robotic systems. Analyze on-road fleet data, identify root causes, reproduce issues through simulation and offline testing, and develop automated investigation workflows. Collaborate with engineering, operations, safety, and model development teams to resolve incidents, improve AV performance, and strengthen safety practices. Communicate findings through dashboards, visualizations, and clear technical explanations.

Machine Learning Engineer needed to enhance ML platform, build infrastructure, and utilize ML frameworks to solve problems in a financial context.

Lead pre-sales activities at Datatonic, shaping data and AI solutions, architecting transformations, and advising clients across various industries. Build trust with C-suite, manage RFPs, and represent the company in strategic engagements.

The Senior Product Manager leads product implementation, collaborates with cross-functional teams, validates roadmaps, and utilizes AI for product improvements. They guide junior team members and represent the product vision during planning and launch sessions.

Embed with deal and litigation teams to scope, configure and deploy legal technology (document review, contract analysis, automation, eDiscovery, transaction management). Advise clients, deliver demos/POCs, drive adoption, capture market insights for product teams, ensure compliance and optimise workflows to improve efficiency and revenue.

The role requires leveraging financial knowledge and analytical skills to develop mathematical models predicting financial market movements, utilizing strong quantitative and computing skills in research.

The role focuses on using advanced statistical and ML techniques to analyze financial market data and generate predictive insights.

The Engineering Manager will lead the AI Platform Team, overseeing infrastructure development, hands-on coding, and supporting ML teams while planning high-impact roadmaps and improving developer experience.

Lead product strategy for AI-powered personalization and recommendation capabilities. Define requirements, partner with Data Science and Engineering to build, test, launch and optimize models, monitor performance, run experiments, prioritize enhancements using analytics, and communicate results to stakeholders while ensuring scalability, compliance and strong customer experiences.

Lead Supply Chain Planning projects, provide expert solutions, develop business, and improve client supply chain processes using tools like SAP IBP and Kinaxis.

The Senior Ecommerce Manager drives revenue growth for brands on marketplaces, manages cross-functional teams, and develops strategic plans to enhance performance and client relationships.

Lead consultative engagements in Life Sciences, focusing on sales operations and CRM. Enhance business development and team management across EU markets.

As a Quantitative Research Analyst, you will develop models, conduct complex data analysis, and work with stakeholders to optimize investment strategies.

As the Global Director of Social Data, you will establish social media measurement strategies, manage analytics, and lead a global team to enhance data-driven decision-making for brands.

Owns roadmap execution and capability definition for an Agentic AI Platform. Translates AI platform strategy into requirements, prioritized roadmaps, reusable agent orchestration and lifecycle capabilities, governance controls, observability, and scalable platform patterns. Partners with engineering, architecture, data, security, legal, compliance, customer success, professional services, and customers. Measures adoption, reliability, safety, quality, cost, and business impact while balancing technical feasibility, security, compliance, and customer value.

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.

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.

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.

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.