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)
• Comfortable working directly with robots and hardware, not just simulators
• Proficient in Python, with familiarity across standard RL/ML frameworks such as JAX, PyTorch, IsaacGym/IsaacLab, or MuJoCo
• An empirical, debugging-first mindset - you care about what actually works on hardware
• Able to move fast and switch between research problems and engineering tasks

Tyson Jay Management Pte Ltd | EA License No.: 24C2479 Ivan Lim | EA Personnel No.: R1109856