Machine Learning Engineer (Robotics, Control Policies) - up to $10,000 + Bonus
Summary
Design and train reinforcement and imitation learning policies for robotic movement and control, running experiments on physical hardware to bridge the sim-to-real gap. Day-to-day work uses Python with PyTorch and JAX, alongside robotics simulators like MuJoCo and IsaacGym.
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