NVIDIA accelerates humanoid robots’ development with the Isaac solution and GR00T blueprint. We’re now looking for a robotics expert, especially in simulation to support this effort. As a Solutions Architect, you’ll collaborate with an exceptional and highly collaborative research team known for influential work in multimodal foundation models, large-scale robot learning, embodied AI, and physics simulation, pushing the frontier of humanoid robotics.

What you will be doing:

• Develop and maintain simulation environments built on frameworks like MuJoCo, and Isaac Lab to support robotics research.

• Implement and test control algorithms and XR teleoperation interfaces for simulated robots.

• Build procedural generation pipelines for diverse environments, object layouts, and robot motions.

• Optimize GPU-based physics simulator performance for large-scale training workloads.

• Import, configure, and validate robot assets in USD format, ensuring successful sim2real transfer.

• Implement Sim2Real pipelines and deploy learned models to physical robots.

What we need to see:

• Bachelor’s degree in Computer Science, Robotics, Engineering, or a related field;

• 3+ years of full-time industry experience on robotics and/or physics simulation;

• Proficiency in languages such as Python, C++, and 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.

Ways to stand out from the crowd:

• Master’s or PhD’s degree in Computer Science, Robotics, Engineering, or a related field;

• Experience at 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.