Senior Research Scientist, Multimodal Foundation Models and Robotics

Nvidia-posted 4 months ago
$184,000 - $356,500/Yr
Full-time • Senior
Santa Clara, CA
5,001-10,000 employees
Computer and Electronic Product Manufacturing
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We are now looking for a Senior Research Scientist focused on Multimodal Foundation Models and Robotics! NVIDIA is searching for an outstanding research scientist to build humanoid robot foundation models and systems in the Generalist Embodied Agent Research (GEAR) group. Everything that moves will eventually be autonomous. Our mission is to build general-purpose embodied agents that learn to explore and master complex skills across the virtual and the physical world. You will work with an amazing and collaborative research team that consistently produces influential works on multimodal foundation models, large-scale robot learning, game AI, and physical simulation. Our past projects include Eureka, VIMA, Voyager, MineDojo, MimicPlay, Prismer, and more. One of our team's most recent milestones includes Project GR00T, a foundation model for humanoid robots. Your contributions will have a significant impact on our moonshot research projects and product roadmaps.

  • Design and implement novel AI algorithms and models for general-purpose humanoid robots and embodied agents
  • Develop large-scale AI training and inference methods for foundation models
  • Optimize and deploy AI models in physical simulation and on robot hardware
  • Collaborate with research and engineering teams across all of NVIDIA to transfer research to products and services
  • A Ph.D. in Computer Science/Engineering, Electrical Engineering, etc., or equivalent research experience
  • 5 years of relevant work/research experience across one or both of these fields: Multimodal Foundation Models and Robotics
  • Outstanding engineering skills in rapid prototyping and model training frameworks (PyTorch, Jax, Tensorflow, etc.)
  • Python is required; C++ and CUDA proficiencies are a big plus
  • Excellent skills in working with large-scale machine learning/AI systems and compute infrastructure
  • Hands-on training experience and publications in robot learning, such as reinforcement learning, imitation learning, classical control methods, etc.
  • Strong programming skills in Python, C++, ROS, and machine learning frameworks like PyTorch
  • Deep understanding of robot kinematics, dynamics, and sensors
  • Ability to safely operate robot hardware, lab equipment, and tools
  • Knowledge of control methods, including PID, model predictive control, and whole-body control
  • Familiarity with physics simulation frameworks such as MuJoCo and Isaac Sim
  • Robot hardware design and hands-on building experience
  • Equity and benefits
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