Reinforcement Learning Engineer, Manipulation (Visa Sponsorship - London, United Kingdom)

Visa sponsorship available, including relocation support to London. A fast-growing humanoid robotics company in London is building general-purpose robots that are already running in real industrial settings. You will join the team that develops the core vision-language-action model used across the company, and apply reinforcement learning to turn that model into dependable manipulation policies. The role is a senior individual contributor position with no people-management track, and the team shares its results through detailed technical write-ups rather than academic papers.

What you'll do

• Train language- and vision-conditioned manipulation policies using reinforcement learning, both in simulation and on physical robots.
• Design large, varied suites of manipulation tasks in simulation.
• Work with teleoperation staff to gather simulated trajectories for behaviour cloning.
• Partner with testing and operations teams to build real-world reinforcement learning training pipelines.
• Develop methods to transfer simulation-trained policies to hardware (sim-to-real).

What you need

• At least 3 years of experience building deep learning systems in industry or research, with shipped models or published work.
• Proven record of solving real problems with reinforcement learning and deep neural networks, in any domain. Robotics experience is not required.
• Hands-on experience training models end to end (architecture, training and inference) in at least one of: large language models, vision-language models, or image/video generative models. Agent building or LLM integration alone does not count.
• Strong Python and PyTorch or JAX, with the ability to profile code, debug numerical issues and write maintainable research code.
• Strong coding fundamentals; the hiring process includes a LeetCode-style coding interview.
• Self-directed, able to take an open-ended problem and drive it forward, and able to document experiments and explain trade-offs clearly.
• Working English.

How to apply

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