Reinforcement Learning Systems Engineer (up to $10,000 Bonus)

Responsibilities

• Develop and iterate on locomotion controllers and motion policies for a legged platform
• Train and evaluate policies in simulation across walking, recovery, stair climbing, and load-bearing behaviors
• Design reward functions, curriculum schedules, and training infrastructure for real-world robustness
• Drive systematic sim-to-real transfer and hardware iteration
• Integrate locomotion outputs with the broader autonomy stack
• Collect and analyze hardware telemetry to guide policy improvement

Requirements

• Strong foundations in reinforcement learning, optimal control, and rigid body dynamics
• Hands-on experience training or deploying locomotion and motion control policies on physical legged robots, gained through industry or research work
• Proficient in Python, with strong JAX or PyTorch experience
• Experience with physics simulation environments such as MuJoCo, Isaac Gym, Genesis, or equivalent
• Practical experience closing the sim-to-real gap on a real platform
• 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