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

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Machine Learning Engineer (Robotics, Control Policies) – up to $10,000 + Bonus