Top Machine Learning Jobs in London

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

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.

The role involves collaborating with research scientists to improve machine learning models for music generation, optimizing training pipelines, and integrating models into production. Responsibilities include ensuring code quality and enhancing internal tools for research efficiency.

Lead the design and implementation of AI solutions, ensuring performance and reliability. Oversee AI strategy, integration, evaluation protocols, and deployment pipelines.

This internship involves experimenting with machine learning, prototyping ideas, exploring AI tools, collaborating with teams, and learning about AI system evaluation.

The role involves developing machine learning models, optimising system performance, and collaborating with diverse teams on innovative projects.

As Executive Director of Toxicology, you'll lead a team at the intersection of Discovery and Development, guiding safety studies and integrating innovative approaches alongside data science and AI to advance drug development.

As a Staff Machine Learning Engineer, you will lead the development of complex ML systems and MLOps pipelines, mentor junior engineers, and ensure scalable and reliable solutions for retail workflows.

As a Principal Product Manager at Mapbox, you'll drive the navigation strategy by collaborating with engineering and cross-functional teams, analyzing data trends, mentoring staff, and engaging with customers to innovate and execute on product solutions.

Design, test, and implement innovative machine learning models to enhance predictive analytics for sports betting based on historical data.

As an MLOps Engineer, you'll build scalable pipelines, manage model deployment, and improve our ML workflow using Python and Kotlin. You'll collaborate on real-world impact projects and integrate new ML models into our product.

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

The Machine Learning Research Engineer will design and implement foundational AI models and infrastructure for multi-omics, focusing on high-performance ML algorithms and scalable data pipelines for biomedical applications.

As an Applied AI Intern, you will support the deployment of AI applications, collaborate with teams on customer projects, and enhance product capabilities based on user feedback.

The role involves developing and optimizing machine learning models and collaborating on research projects with quantitative researchers.

Conduct research using machine learning techniques to develop investment strategies, leveraging extensive data and computational resources.

As a Machine Learning Performance Engineer, you'll optimize large-scale workloads across GPU and CPU infrastructure, collaborating with researchers and engineers to improve performance and capabilities of research workloads.

As a Senior Machine Learning Engineer, you will lead the development of AI systems, design production-grade ML software, and mentor junior engineers while engaging with clients on technical strategies.

As a Machine Learning Engineer at Faculty, you'll build and deploy AI solutions, collaborate with teams, and advise stakeholders on technical aspects.

The Senior ML Systems Engineer will build and maintain the training framework for large-scale language models, focusing on distributed training and performance optimization.

The ML Platform Engineering Manager will lead a team to build scalable AI infrastructure, overseeing backend systems and guiding engineers while fostering collaboration between teams.

Design and maintain ML code for LLM safety, implement techniques for robust evaluation, and collaborate with product and engineering teams.

The role involves owning an LLM safety domain, generating synthetic data, training LLMs, conducting benchmarking, and developing techniques for safer models. Co-author research papers with the team.

Design and implement reusable ML templates and MLOps tooling for scalable ML solutions, collaborating with ML teams to improve system quality and delivery.

Develop and optimize large-scale machine learning models for drug discovery, maintain workflows, ensure model reproducibility, and collaborate with teams.