Senior Machine Learning Research Engineer

About the Role

What You'll Do

What We're Looking For

What We Offer

• 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.

Qualifications

• 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.

Preferred Qualifications

• 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.
• 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.