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 - our rapidly developed humanoid platform being deployed in real industrial environments - and we’re growing the team to take it even further.
About the RoleWe are looking for a strong Deep Learning Engineer to join our Autonomy team and develop the learning-based algorithms that power dynamic locomotion and whole-body control on our humanoid robots.
You will develop robust, reusable motion capabilities that enable the robot to move dynamically, maintain balance, interact with its environment, and combine locomotion with manipulation. This includes building the underlying learning-based motion primitives that can be composed into increasingly complex whole-body behaviors.
You will work closely with robotics, controls, simulation, and machine learning engineers to turn learned behaviors into reliable capabilities running as part of the robot's real-time control stack.
Design and train learning-based locomotion and whole-body control policies for dynamic robotic behaviors.
Develop motion primitives using different learning based generative models.
Apply reinforcement learning, imitation learning, and other learning-based approaches to motion generation and control.
Build scalable simulation, data generation, and training pipelines using platforms such as Isaac Lab and MuJoCo.
Develop methods for robustness, adaptation, and sim-to-real transfer, including domain randomization and learning from real-world robot data.
Deploy, evaluate, debug, and fine-tune learned policies on physical humanoid robots.
Optimize policy inference and training systems for the performance and latency requirements of real-time control.
Integrate learned policies into the broader robot control and autonomy stack.
BS, MS, or PhD in Robotics, Machine Learning, Computer Science, or a related technical field.
3+ years of experience developing machine learning systems in industry or research.
Strong understanding of deep learning and modern policy architectures, including training, inference, experimentation, and debugging.
Hands-on experience with RL and at least one additional area such as imitation learning, generative models, sequence models, or large-scale representation learning.
Strong Python and PyTorch experience.
Experience with physics-based simulation environments such as Isaac Lab, Isaac Sim, MuJoCo, or similar platforms.
Ability to reason about learning algorithms in the context of dynamics, control, and physical systems.
Nice to have:
Experience applying reinforcement learning or imitation learning to legged robots, humanoids, or other complex physical systems.
Experience with whole-body control, locomotion, motion imitation, or loco-manipulation.
Experience deploying learned policies on real robotic systems and debugging the sim-to-real gap.
Familiarity with motion capture, trajectory optimization, model-based control, or learning from demonstrations.
Experience training policies at scale across large numbers of simulated environments.
Publications at NeurIPS, ICML, ICLR, CoRL, RSS, CVPR, or equivalent contributions to impactful open-source robotics or machine learning projects.
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.
