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PhD Research Intern, Generalist Embodied Agents Research - 2027 will join the Generalist Embodied Agent Research group to develop humanoid robot foundation models and systems. The role involves designing and implementing novel AI algorithms for general-purpose embodied agents, developing large-scale training and inference methods for foundation models, and optimizing model deployment on physical hardware and in simulations. Candidates must possess strong engineering skills in rapid prototyping and model training frameworks like PyTorch, Jax, or Tensorflow. Required technical skills include Python, with C++ and CUDA as pluses. The work focuses on the technical challenge of creating agents that master complex skills across virtual and physical environments. Relevant expertise includes multimodal foundation models, such as LLMs and vision-language models, or robotics fields like reinforcement learning, imitation learning, and control methods using Mujoco or Isaac suite.
Design and implement novel AI algorithms for general-purpose humanoid robots and embodied agents.
Develop large-scale training and inference methods for multimodal foundation models.
Optimize and deploy AI models within physical simulations and on actual robot hardware.
Build systems that enable agents to learn complex skills in virtual and physical environments.
Translate research findings into practical products and services across NVIDIA's ecosystem.
Utilize frameworks like PyTorch, Jax, or TensorFlow to prototype and train machine learning models.
Apply knowledge of robot kinematics, dynamics, and control methods to develop autonomous behaviors.
What we're looking for
Must be pursuing a PhD degree in Computer Science, Engineering, Electrical Engineering, or a related field.
Proficiency in Python is required.
C++ and CUDA proficiencies are preferred.
Outstanding engineering skills in rapid prototyping and model training frameworks like PyTorch, Jax, or TensorFlow.
Experience with large-scale machine learning/AI systems and computer infrastructure.
Experience with at least one of the following: multimodal foundation models or robotics.
Hands-on training experience and publications in LLMs, vision-language models, video generative models, diffusion models, or action-based transformers (preferred).
Hands-on training experience and publications in robot learning, such as reinforcement learning, imitation learning, or classical control methods (preferred).
Generative AI
Multimodal Foundation Models
World Models
Python
PyTorch
Computer Vision
Reinforcement Learning
Distributed Training
Robotics
Autonomous Driving
Video Understanding
Simulation
VLMs
Policy Learning
Python
C++
CUDA
PyTorch
Deep Learning
Reinforcement Learning
Robotics
Autonomous Vehicles
Simulation
Machine Learning
Parallel Programming
Closed-loop Training
Verification and Validation
Generative AI
Machine Learning
Computer Vision
NLP
PyTorch
Python
C++
CUDA
Parallel Programming
Multimodal Data
Deep Learning
Research
Rapid Prototyping
Generative AI
Multimodal Foundation Models
World Models
Python
PyTorch
Computer Vision
Reinforcement Learning
Distributed Training
Robotics
Autonomous Driving
Video Understanding
Simulation
VLMs
Policy Learning
C
C++
Python
Deep Learning
Computer Architecture
Distributed Systems
Operating Systems
Compilers
LLM Training
Inference
Simulation
Modeling
Benchmarking
AI/ML Systems
System Software