Reinforcement Learning Engineer - Manipulation

Humanoid; Greater London; On-site; GBP 70,000 - 120,000; Full time

Benefits offered by this job

Stock options with upside

30+ paid days off

Private healthcare

Pension scheme 8% total contribution

Free daily breakfast and snacks

World‑class collaborators

Job summary

Humanoid is seeking a Reinforcement Learning Engineer to join the Autonomy team in London. You will apply RL to build robust manipulation policies in both simulation and real‑world environments.

You'll work with Python, PyTorch/JAX, and cutting‑edge robotics software to push the frontier of humanoid autonomy while collaborating with world‑class engineers and researchers.

Qualifications

• 3+ years building deep‑learning systems with shipped models or publications.
• 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.

Responsibilities

• 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.

Skills

Deep learning

Reinforcement Learning

Python

PyTorch

JAX

LLMs/VLMs

Tools

PyTorch

JAX

Job description

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.

About the Role

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.

What You'll Do

• 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.

What We're Looking For

• 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 (π) models, or similar open VLA frameworks.

What We Offer

• Competitive equity: stock options with meaningful upside as we scale.
• 30+ paid days off, including 23 days of annual leave, all UK bank holidays, and additional company closure days (including Christmas–New Year shutdown).
• Private healthcare, including virtual and in‑person care.
• Pension scheme with 8% total contribution (5% employee, 3% employer) on full earnings.
• Free daily breakfast, catered lunch, and snacks in‑office.
• Work at the frontier - collaborate daily with world‑class engineers, researchers, and product experts building the next generation of AI and humanoid robotics.
• Real ownership - direct access to founding leadership, meaningful input on product direction, and the ability to drive key initiatives from day one.