Machine Learning Engineer (Robotics, Control Policies) - up to $9,000 + Bonus

ResponsibilitiesDesign and train reinforcement learning and imitation learning policies for movement and control tasksRun experiments on physical hardware and close the sim-to-real gap through systematic debugging and domain adaptationBuild and maintain simulation environments and data pipelines that support fast policy iterationInstrument deployments and analyse failure modes, feeding what you learn back into trainingWork closely with hardware and firmware engineers to understand physical constraints and improve policy robustnessRequirementsAround 2 to 3 years of relevant experience; exceptional recent graduates with a genuinely strong portfolio and internship background will also be consideredStrong 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 simulatorsProficient in Python, with familiarity across standard RL/ML frameworks such as JAX, PyTorch, IsaacGym/IsaacLab, or MuJoCoAn empirical, debugging-first mindset - you care about what actually works on hardwareAble to move fast and switch between research problems and engineering tasksTyson Jay Management Pte Ltd | EA License No.: 24C2479 Ivan Lim | EA Personnel No.: R1109856