Advanced Control Systems Engineer (Up To $10,000 + Bonus)

Responsibilities Develop and deploy manipulation policies for dexterous task execution on physical robots Build grasp planning and contact-rich control pipelines that generalize across varied objects and environments Design and run data collection and teleoperation infrastructure to feed policy training at scale Train manipulation policies using imitation learning, reinforcement learning, or hybrid approaches Integrate manipulation with the perception stack and broader autonomy pipeline Diagnose failure modes on hardware systematically and drive improvements Requirements Strong foundations in robotics, control theory, and motion planning Hands-on experience with manipulation systems on real robotic platforms, gained through industry or research work Proficient in Python and C++, with PyTorch or JAX experience Experience taking manipulation work from prototype to hardware deployment Exposure to data collection or teleoperation workflows for policy training Candidates with limited industry experience are welcome to apply, provided this is supported by strong relevant academic or research work, such as a thesis, publications, or hands-on robotics projects Tyson Jay Management Pte Ltd | EA License No.: 24C2479 Ivan Lim | EA Personnel No.: R1109856