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

NVIDIA

Reinforcement learning for robotics control
Isaac lab or similar simulation platforms
C++ for real-time systems and python
This role focuses on building behavior foundation models to train large-scale controllers for humanoid robots

Job Summary

  • This role focuses on building behavior foundation models to train large-scale controllers for humanoid robots.
  • The engineer will deploy learned whole-body control policies on physical hardware while optimizing the runtime stack for low-latency execution.
  • Success requires closing the gap between simulation and reality through rigorous testing, debugging, and system identification.

Matching Summary

This role focuses on building behavior foundation models to train large-scale controllers for humanoid robots.

Skills & Requirements

Must-have

  • Reinforcement Learning for robotics control
  • Isaac Lab or similar simulation platforms
  • C++ for real-time systems and Python
  • Deploying policies on physical robot hardware
  • Humanoid or legged robot motion tracking
  • Sim-to-real system identification

Nice-to-have

  • Experience with VLA outputs integration
  • Understanding of thermal limits in actuators
  • Background in neural network architectures
  • Proficiency in contact instability debugging

Key Requirements

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

Work Rights

Not specified

Tailored Resume

Cover Letter