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 e...

Ready to Apply?

Submit your application today and join our talented team at Tyson Jay.