Top Machine Learning Jobs in London

Lead a team of ML Scientists to develop and implement models for Depop's search engine, collaborating with cross-functional teams while ensuring responsible-AI practices.

Lead the GPU kernel development team at Wayve, focusing on deploying ML models efficiently onto autonomous vehicles, while mentoring engineers.

The role involves open-sourcing models and maintaining libraries, optimizing performance, and collaborating with the open-source community. Requires expertise in machine learning and strong programming skills.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead the Applied ML team to create AI solutions for enterprises, manage ML engineers, and collaborate with customers and executives on initiatives.

The Head of Data & AI leads initiatives in data and AI, focusing on remediation, governance, infrastructure development, and collaboration with various teams to ensure ethical AI use and data-driven decision-making.

Design and build retrieval tools and APIs for Knowledge Graphs. Optimize automated querying systems and manage ML models lifecycle. Collaborate with teams to enhance data retrieval capabilities.

The Solutions Marketing Manager will develop joint value propositions and messaging for Alteryx solutions, creating go-to-market assets and collaborating with teams to drive leads and revenue.

The Global Key Account Manager at Feedzai will develop financial services relationships, drive sales, and manage client relationships, focusing on fraud prevention and AML solutions.

As a Data Scientist at Wave, you will optimize user initiatives, perform advanced analytics, and develop machine learning solutions to enhance user engagement and business impact.

The role involves designing, building, and maintaining mobile software for iOS and Android while collaborating with cross-functional teams to improve user experience and system quality.

The Machine Learning Platform Engineer will manage cloud infrastructure, support AI researchers, and deploy models in production, focusing on MLOps and CI/CD practices.

Lead the design and deployment of large-scale, automated systems using ML/AI technologies, ensuring service reliability and optimizing workflows within the organization.

Lead engineering teams to enhance search and recommendations systems at ASOS, focusing on scalability, technical excellence, and collaboration across product and design teams.

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

Interns will work with the AI Inference team to improve serving latency, support new models, and optimize inference across the stack.

The Senior Machine Learning Engineer will enhance search quality, build core components, train models, and collaborate with various teams.

Design and implement reusable ML templates and MLOps tooling, collaborate with ML teams, and improve ML systems across ASOS.

The role involves developing an ML research and deployment pipeline, implementing best practices in model and feature management, and ensuring robustness in production through effective solutions and collaboration.

The Product Manager will lead the credit card product strategy, working on mobile app features, customer acquisition, and analytics to drive growth.

You will develop backend software for AI/ML applications, define and deploy infrastructure, integrate components, and ensure high software quality through testing and documentation.

The Partnerships Manager will drive partner strategy and revenue growth in EMEA, build executive relationships, and manage partner performance metrics and governance using BI tools.

Develop and deploy graph-based ML models for supply chain risk analysis, collaborate with teams, and lead AI advancements.

As an ML intern, work on a significant research project utilizing machine learning techniques, supported by experienced mentors, culminating in a presentation of findings.

As a Machine Learning Researcher, you will design and evaluate models for biological data, partner with clinicians, and run experiments for impactful research.