Humanoid is building the world's most capable, commercially-scalable, and safe humanoid robots
HMND-01 is their humanoid platform deployed in real industrial environments
The role sits in the Autonomy team
Responsibilities
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
Requirements
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 RL 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
Conditions
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 with world-class engineers, researchers, and product experts
Real ownership with direct access to founding leadership