Reinforcement Learning Engineer - Manipulation

We’re hiring a Reinforcement Learning Engineer to join our Autonomy team based in London. In this role you will leverage reinforcement learning in both simulation and physical reality to build highly performant and robust manipulation policies.

3+ years building deep-learning systems (industry or research) with shipped models or published artifacts to show for it.

Hands-on with at least one of: LLMs, VLMs, or image/video generative models - architecture, training, and inference.

Experience solving real problems using reinforcement learning with deep neural networks in any domain.

Strong Python + PyTorch/JAX; you can profile, debug numerics, and write maintainable research code.

You are self-driven, pro-active, communicate efficiently, document experiments clearly and communicate trade-offs crisply.

Nice to have

Experience with simulators for robotics (Isaac Sim, MuJoCo etc.)

Experience in RL for robotics.

Experience building infrastructure for large-scale RL (e.g. using ray).

Publications at ICLR/ICML/NeurIPS or equivalent open-source contributions.

Familiarity with OpenVLA, Physical Intelligence (p) models, or similar open VLA frameworks.

23 days of annual leave 15 days of paid sick leave Paid company holidays Fully funded private healthcare Equity Pension scheme with 8% contribution Free daily breakfast, catered lunch, and snacks in-office

Here at Humanoid, we believe in a future where robots amplify human potential. That’s why we’ve set out on a mission to build the world’s most capable, commercially-scalable, and safe humanoid robots. We’re bringing that mission to life with HMND-01 Alpha - our rapidly developed humanoid platform now running in real industrial pilots - and we’re growing the team to take it even further., Meaningful time off to rest and recharge: 23 days of annual leave (accrued), 15 days of paid sick leave, and paid company holidays.

Fully funded private healthcare for UK employees, with broad provider access, virtual and in-person care, and strong mental health and serious illness support.

Equity included-we believe builders should share in what they build.

Pension scheme with a total 8% contribution (5% employee, 3% employer) on full earnings.

Free daily breakfast, catered lunch, and snacks in-office.

Collaboration with top-tier engineers, researchers, and product experts in AI and robotics.

Train language-vision conditioned manipulation policies via reinforcement learning (RL) in simulation and in the real world.

Construct challenging and diverse suites of manipulation tasks in simulation.

Partner with teleoperations to collect trajectories in simulation for behavior cloning.

Partner with testing and operations to establish real-world RL training pipelines.

Experiment with various ways of bringing policies trained in simulation to the real world.