Responsibilities

• Design and train reinforcement learning and imitation learning policies for movement and control tasks
• Run experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptation
• Build and maintain simulation environments and data pipelines that support fast policy iteration
• Instrument deployments and analyse failure modes, feeding what you learn back into training
• Work closely with hardware and firmware engineers to understand physical constraints and improve policy robustness

Requirements

• Around 2 to 3 years of relevant experience; exceptional recent graduates with a genuinely strong portfolio and internship background will also be considered
• Strong foundations in reinforcement learning or imitation learning, with hands-on experience training policies that run on real physical systems (not simulation only)