Reinforcement Learning Engineer - Manipulation

London
- Fully onsite Full time

Join a forward-thinking autonomy team developing advanced humanoid robots. In this role, you will develop and train reinforcement learning models for robotic manipulation tasks, working both in simulation and real-world environments. Collaborate across teams to create robust manipulation policies, contribute to deployment pipelines, and innovate solutions to bridge simulation and reality performance gaps.

• Train language-vision conditioned manipulation policies using reinforcement learning in simulation and real-world settings
• Design and implement diverse and challenging manipulation tasks in simulation
• Collaborate with teleoperation teams to collect trajectories for behavior cloning
• Work with testing and operations to establish real-world RL training pipelines
• Experiment with methods to transfer policies from simulation to physical robots
• 3+ years building deep learning systems with delivered models or publications
• Hands-on experience with at least one of: large language models, vision-language models, or image/video generative models
• Experience applying reinforcement learning with deep neural networks in practical domains
• Strong proficiency in Python and PyTorch or JAX, including debugging and profiling numerical code
• Self-driven with clear communication, documentation, and concise presentation of trade-offs
• Desirable: experience with robotics simulators (e.g., Isaac Sim, Mu Jo Co )
• Desirable: experience in RL for robotics and large-scale RL infrastructure (e.g., ray)
• Desirable: publications at major ML conferences or contributions to open source

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London
- Fully onsite, Full time. Confirm employment terms and any clearance requirements with your consultant.

reinforcement learning robotics deep learning simulation manipulation Python PyTorch machine learning