Top Machine Learning Jobs in London

The Senior AI/ML Engineer at Nansen will develop AI models for blockchain data analysis, collaborating with teams to enhance platform capabilities and mentor junior engineers.

As a Senior AI/ML Architect, you'll design and implement scalable ML solutions, guide clients from concept to production, and work alongside technical leaders.

The Machine Learning Engineer will research algorithms for networking, develop ML infrastructure, maintain documentation, and assist in technical communication.

The role involves developing and supporting data processing pipelines, collaborating with ML Engineers, and improving infrastructure while mentoring team members.

The Senior Machine Learning Engineer will lead the deployment of ML models, design scalable systems, mentor team members, and collaborate with customers to solve engineering challenges.

The Staff Machine Learning Engineer will develop and deploy scalable ML models, mentor junior engineers, and contribute to technical strategy.

Lead the development and optimization of AI and machine learning models, mentor junior engineers, and enhance AI-driven features for the platform.

The Senior AI/ML Consultant will deliver enterprise-level solutions in AI and Machine Learning, engage with clients, and contribute to community growth.

The AI/ML Engineer will develop and implement ML systems for secure-code understanding, manage infrastructure for LLMs, optimize models, and conduct research.

Lead the Applied AI Team in developing and deploying ML systems for clinical care, mentoring engineers, and driving initiatives across the AI stack.

Own and drive ML solutions for clinical care at scale, including LLM fine-tuning and production deployment, collaborating across teams.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Waymo seeks a summer intern to contribute to projects, implement research ideas, and run training and evaluation experiments in autonomous driving technology.

Develop and enhance systems for data ingestion and enrichment, improve query performance, and collaborate with teams on technical requirements.

The Software Engineer will manage and deploy AWS infrastructure, develop ML and NLP solutions, and collaborate on AI projects supporting financial data analytics.

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 development of AI/ML infrastructure for simulations, collaborating with teams on model scaling, system design, and mentoring junior 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.

As a Lead Machine Learning Engineer, you will guide technical direction and deliver innovative AI projects, ensuring architectural soundness and fostering team growth.

As a Machine Learning Engineer, you will design, build, and deploy ML systems, manage AI project methodologies, and support delivery with technical expertise, collaborating with cross-functional teams and stakeholders.

Lead the development and deployment of AI systems, designing scalable ML software and infrastructure while mentoring junior engineers and advising clients.

The Senior Machine Learning Engineer will design and implement AI/ML solutions, optimize performance, and collaborate with teams to enhance product development.

Lead design, optimization, and deployment of automation efforts using large-scale ML/AI systems, ensuring scalable and efficient infrastructure.

As a Senior Data Engineer, you'll design scalable data pipelines, collaborate with engineers and product teams, and evolve the architecture to support growth in users and data.

The Machine Learning Engineer will build, refine, and optimize AI/ML prototypes while collaborating with teams on technical documentation, deployment, and model integration.