Research Engineer, Robot Learning

1X • San Carlos, CA

3d • $130,146 - $300,000

About The Position

Develop sim2real reinforcement learning (RL) training pipelines for humanoid robots and train and test robot policies for in-home humanoid robots. Optimize training pipelines to improve performance and training speed. Model physical systems—including robot dynamics, sensor behavior, and contact parameters—to reduce the gap between simulation and physical systems. Collect, clean, and retarget human motion data for RL training pipelines. Support testing and debugging RL policies in simulation and on physical hardware.

Requirements

• Master’s degree or foreign degree equivalent in Robotics, AI/Machine Learning, Mechanical or Electrical Engineering, Computer Science, or a related field.
• One year of experience in a Robotics & Machine Learning occupation or a related occupation in the job offered.
• Experience and/or education in rigid-body dynamics and/or dynamical systems for training, deploying, and testing reinforcement-learned policies for humanoid robots.
• Experience and/or education implementing production-grade Python code for reinforcement learning and other machine learning training pipelines.
• Experience and/or education with reinforcement-learning algorithms, techniques, and sim2real approaches in robotics for human-like capabilities, including whole-body loco-manipulation.
• Experience and/or education implementing and debugging real-time software in C++ deployed on robots to meet real-time constraints.
• Experience and/or education modeling physical systems, including multi-body hybrid dynamic systems with contact, in simulation to identify simulation-to-hardware gaps and accurately model hardware in simulation.
• Experience and/or education performing root-cause analysis for robotic systems and quickly troubleshooting issues.
• Experience and/or education with forward and inverse kinematics for humanoid system training using human motion data.

Responsibilities

• Develop sim2real reinforcement learning training pipelines for humanoid robots.
• Train and test reinforcement learning policies for in-home humanoid robots.
• Optimize training pipelines for performance and training speed.
• Model physical systems, including robot dynamics, sensors, and contact parameters, to reduce the simulation-to-hardware gap.
• Collect, clean, and retarget human motion data for reinforcement learning training pipelines.
• Support testing and debugging reinforcement learning policies in simulation and on physical hardware.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level