About the role
Role: Reinforcement Learning Engineer – Manipulation
Company: Humanoid
Location: London, UK (King’s Cross) – on-site, five days a week
Job Type: Full-time
About Humanoid
Humanoid is building the world’s most capable, commercially scalable and safe humanoid robots. Its platform, HMND-01, is already deployed in real industrial environments, which makes Humanoid the most serious humanoid robotics company in Europe.
- 300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots.
- Offices in London (HQ, King’s Cross), Boston, Vancouver and San Diego.
- Values: Speed, Ambition, Passion, Trust.
About the role
The VLA Core team builds the base vision-language-action model the whole company runs on, covering pre-training, world models and RL. In this role you use reinforcement learning, in simulation and on real hardware, to turn that model into high-performing, robust manipulation policies.
This is a senior individual contributor role with no management track. Robotics experience is not required: very few people have done RL on real robots, so strong RL in any domain is what matters. The team is research-adjacent, but the output is policies that work on the robot. Results are shared as blog posts, not papers.
What you are expected to do
- Train language- and vision-conditioned manipulation policies with RL, in simulation and in the real world.
- Build challenging and diverse suites of manipulation tasks in simulation.
- Work with Teleoperations to collect trajectories in simulation for behaviour cloning.
- Work with Testing and Operations to set up real-world RL training pipelines.
- Find ways to bring policies trained in simulation to the real world (sim-to-real).
What you need to succeed
- 3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.
- You have solved real problems with RL and deep neural networks, in any domain.
- You have trained models yourself (architecture, training and inference) on at least one of: LLMs, VLMs, image or video generative models. Agent building or LLM integration work alone is not enough.
- Strong Python and PyTorch or JAX. You can profile, debug numerics and write maintainable research code.
- Strong coding fundamentals. The process includes a LeetCode-style coding round, so come prepared.
- You are proactive: you can take an open, messy problem and drive it without waiting for a spec. You document experiments clearly and explain trade-offs crisply.
- Working English.
Nice to have
- Experience with robotics simulators (Isaac Sim, MuJoCo, etc.).
- Experience applying RL to robotics.
- Experience building infrastructure for large-scale RL (e.g. Ray).
- Publications at ICLR, ICML, NeurIPS or equivalent open-source contributions.
- Familiarity with OpenVLA, Physical Intelligence (π) models or similar open VLA frameworks.
Interview process
- Intro call with Igor, hirehire’s AI researcher.
- HR screen with Humanoid.
- Coding round (LeetCode-style).
- RL specialization interview.
- Technical execution interview: how you work, collaborate and drive problems.
- Optional chat with a tech leader.
What you get
- Salary: a wide range, set by the level you show in the interviews.
- Equity: stock options with meaningful upside as Humanoid scales.
- 30+ paid days off: 23 days annual leave, all UK bank holidays and a company shutdown between Christmas and New Year.
- Private healthcare: fully funded, including virtual and in-person care.
- Pension: 8% total contribution (5% employee, 3% employer) on full earnings.
- Daily food: free breakfast, catered lunch and snacks in the office.
- Real ownership: direct access to founding leadership, real input on product direction and room to drive key initiatives from day one.
- Visa sponsorship and relocation to London.