RL Engineer | Humanoid

Role: Reinforcement Learning Engineer – Manipulation Company: Humanoid https://thehumanoid.ai/ 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.