Machine Learning Engineer (Robotics, Control Policies)

up to $9,000 base + Bonus

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

Pay: $6,000.00 - $9,000.00 per month

Work Location: In person