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

Invidia

Reinforcement learning for robotics control
Humanoid whole-body control experience
Isaac lab or similar simulation platforms
The role focuses on building behavior foundation models for humanoid robots to enable stable and expressive whole-body movement

Job Summary

  • The role focuses on building behavior foundation models for humanoid robots to enable stable and expressive whole-body movement.
  • Candidates will deploy learned control policies on physical hardware while optimizing the runtime stack for low-latency execution.
  • Success requires bridging the gap between simulation and reality through rigorous system identification and debugging across the full stack.

Matching Summary

The role focuses on building behavior foundation models for humanoid robots to enable stable and expressive whole-body movement.

Skills & Requirements

Must-have

  • Reinforcement learning for robotics control
  • Humanoid whole-body control experience
  • Isaac Lab or similar simulation platforms
  • C++ for real-time robotics systems
  • Python for training and experimentation
  • Sim-to-real deployment strategies

Nice-to-have

  • Experience with VLA model outputs
  • Understanding of rigid-body dynamics
  • Hardware-in-the-loop testing expertise
  • Low-latency runtime optimization skills

Key Requirements

  • PhD in Robotics, ML, 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