humanoid - Senior Machine Learning Research Engineer

London, United Kingdom+ Equity2w ago

In Office Senior EMEA Artificial Intelligence Robotics Research Engineer Machine Learning Engineer Learning & Development

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. • 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. • 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.

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.

Benefits

• 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.