PhD Research Intern: Generative AI for Physical AI

NVIDIA Corporation; Santa Clara (CA); Hybrid; USD 52,000 - 129,000; Full time

Benefits offered by this job

Intern benefits

Job summary

NVIDIA Corporation invites PhD interns to work on Generative AI for Physical AI in a cutting-edge setting in Santa Clara, CA. This internship focuses on developing and evaluating multimodal models for embodied AI across robotics, autonomous systems, and smart environments.

You will collaborate with researchers and engineers, publish findings at top venues, and contribute to NVIDIA products while gaining hands-on experience with large-scale foundations models and video-based AI tasks.

Qualifications

• Currently pursuing a PhD in CS, EE, Robotics, or related fields.
• Strong background in generative AI, computer vision, multimodal learning, robotics, or reinforcement learning.
• Prior publication record and research experience.
• Strong Python and PyTorch skills.

Responsibilities

• Conduct research in generative AI, multimodal foundation models, world models, and embodied AI.
• Develop algorithms for video understanding/generation, action-conditioned simulation, multimodal reasoning, and policy learning.
• Train and evaluate large-scale models using video, image, language, and robotics or autonomous driving data.
• Collaborate with researchers and engineers across AI, robotics, simulation, and graphics teams.
• Publish research at top conferences and transfer innovations into NVIDIA products.

Skills

Python

PyTorch

Generative AI

Computer Vision

Multimodal Learning

Robotics

Education

PhD in CS/EE/Robotics

Job description

NVIDIA Corporation invites PhD interns to work on Generative AI for Physical AI in a cutting-edge setting in Santa Clara, CA. This internship focuses on developing and evaluating multimodal models for embodied AI across robotics, autonomous systems, and smart environments.

You will collaborate with researchers and engineers, publish findings at top venues, and contribute to NVIDIA products while gaining hands-on experience with large-scale foundations models and video-based AI tasks.