Senior Research Engineer, Simulation

NVIDIA · Semiconductors · Santa Clara, CA

NVIDIA is seeking a Senior Research Engineer specializing in physics simulation for their General Embodied Agent Research (GEAR) group, focusing on Project GR00T, an initiative to build foundation models and full-stack technology for humanoid robots. The role involves developing and optimizing simulation environments, implementing control algorithms, building procedural generation pipelines, and deploying learned models to physical robots, with a strong emphasis on sim2real transfer.

What you'd actually do

  1. Develop and maintain simulation environments built on frameworks like MuJoCo, and Isaac Lab to support robotics research.
  2. Implement and test control algorithms and XR teleoperation interfaces for simulated robots.
  3. Build procedural generation pipelines for diverse environments, object layouts, and robot motions.
  4. Optimize GPU-based physics simulator performance for large-scale training workloads.
  5. Import, configure, and validate robot assets in USD format, ensuring successful sim2real transfer.

Skills

Required

  • Bachelor’s degree or above in Computer Science, Robotics, Engineering, or a related field
  • 10+ years of full-time industry experience on robotics and/or physics simulation
  • Proven experience with one or more physics simulators such as MuJoCo, Isaac Sim, PyBullet, Drake, or Gazebo
  • Deep knowledge of state-of-the-art simulation techniques, such as accurate contact dynamics for manipulation and locomotion, and photorealistic rendering for perception.
  • Expertise in generating simulation assets, task definitions, and building Gym-style APIs to support neural network training.

Nice to have

  • Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field
  • Experience at autonomous driving or humanoid robotics companies on physics simulation
  • Hands-on experience with deploying and debugging neural network models on robotic hardware
  • Expertise at reinforcement learning and neural network training
  • Demonstrated Tech Lead experience, coordinating a team of robotics engineers and driving projects from conception to deployment
  • Contributions to popular open-source simulation frameworks or research publications in top-tier conferences, such as ICRA, IROS, RSS, CoRL.

What the JD emphasized

  • 10+ years of full-time industry experience on robotics and/or physics simulation
  • Proven experience with one or more physics simulators such as MuJoCo, Isaac Sim, PyBullet, Drake, or Gazebo
  • Deep knowledge of state-of-the-art simulation techniques, such as accurate contact dynamics for manipulation and locomotion, and photorealistic rendering for perception.
  • Expertise in generating simulation assets, task definitions, and building Gym-style APIs to support neural network training.

Other signals

  • foundation models for humanoid robots
  • large-scale robot learning
  • embodied AI
  • physical simulation