Full-stack Solution Engineer: Humanoid Whole-body Control And Loco-manipulation

Nvidia Corporation

Reinforcement learning for robotics control
Humanoid whole-body motion tracking
Isaac lab or similar simulation platforms
You will help train large-scale controllers to serve as reliable behavior foundations on real humanoid robots

Job Summary

  • You will help train large-scale controllers to serve as reliable behavior foundations on real humanoid robots.
  • The role involves developing planners that convert task-level inputs into motion targets for whole-body policy tracking.
  • You will diagnose and close the gap between policy behavior in simulation and actual hardware performance.

Matching Summary

You will help train large-scale controllers to serve as reliable behavior foundations on real humanoid robots.

Skills & Requirements

Must-have

  • Reinforcement Learning for robotics control
  • Humanoid whole-body motion tracking
  • Isaac Lab or similar simulation platforms
  • C++ for real-time robotics systems
  • Python for training and experimentation
  • Deploying policies on physical robot hardware

Nice-to-have

  • Experience with domain randomization techniques
  • Understanding of rigid-body dynamics
  • Familiarity with VLA model outputs
  • Proficiency in GPU utilization optimization
  • Background in curriculum learning strategies

Key Requirements

  • PhD in Robotics, ML, CS, EE, ME, or related field
  • At least 3 years of research and engineering experience
  • Strong background in Reinforcement Learning for Control

Work Rights

Not specified

Tailored Resume

Cover Letter