RL Engineer | Humanoid

<p style="min-height:1.5em"><strong>Role:</strong> Reinforcement Learning Engineer – Manipulation<br /><strong>Company:</strong> <a target="_blank" rel="noopener noreferrer nofollow" href="https://thehumanoid.ai/">Humanoid</a><br /><strong>Location:</strong> London, UK (King’s Cross) – on-site, five days a week<br /><strong>Job Type:</strong> Full-time</p><p style="min-height:1.5em"><strong>About Humanoid</strong></p><p style="min-height:1.5em">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.</p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">300+ people, 34+ nationalities, flat structure: your decisions ship directly into real robots.</p></li><li><p style="min-height:1.5em">Offices in London (HQ, King’s Cross), Boston, Vancouver and San Diego.</p></li><li><p style="min-height:1.5em">Values: Speed, Ambition, Passion, Trust.</p></li></ul><p style="min-height:1.5em"><strong>About the role</strong></p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em"><strong>What you are expected to do</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">Train language- and vision-conditioned manipulation policies with RL, in simulation and in the real world.</p></li><li><p style="min-height:1.5em">Build challenging and diverse suites of manipulation tasks in simulation.</p></li><li><p style="min-height:1.5em">Work with Teleoperations to collect trajectories in simulation for behaviour cloning.</p></li><li><p style="min-height:1.5em">Work with Testing and Operations to set up real-world RL training pipelines.</p></li><li><p style="min-height:1.5em">Find ways to bring policies trained in simulation to the real world (sim-to-real).</p></li></ul><p style="min-height:1.5em"><strong>What you need to succeed</strong></p><ul style="min-height:1.5em"><li><p style="min-height:1.5em">3+ years building deep learning systems in industry or research, with shipped models or published work to show for it.</p></li><li><p style="min-height:1.5em">You have solved real problems with RL and deep neural networks, in any domain.</p></li><li><p style="min-height:1.5em">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.</p></li><li><p style="min-height:1.5em">Strong Python and PyTorch or J