Senior Solutions Architect, Simulation
NVIDIA
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.
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