PhD Research Intern, Cross-Disciplinary Vision Science - 2026

Nvidia•Santa Clara, CA

53d•$30 - $94

About The Position

NVIDIA has been transforming computer graphics and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology-and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent. As an NVIDIAn, you'll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. We, the Human Performance and Experience (HPX) team at NVIDIA Research, strive to lead AI innovation by deeply understanding humans. We are looking for individuals who are passionate about cross-disciplinary research in vision between humans and machines. We expect candidates with backgrounds in computer vision, human vision science, robotic vision, and related fields to be well-suited to this position. You will be working in a team of research scientists and engineers from varied backgrounds and closely collaborating with product engineers. Join us in transforming the future of AI and human interaction.

Requirements

Currently pursuing a Ph.D. in the field of Cognitive Science, Neuroscience, Computer Science/Engineering, Electrical Engineering, or related fields..
Experience in cross-disciplinary research that applies knowledge from human vision to drive innovations in AI and computer vision, demonstrated by one or more lead-author publications.
Proficiency with Python.
Experience with AI model training and evaluation framework PyTorch or similar.
Comfortable with modern software development, version-control systems, like git(hub/lab).
Familiarity with and interest in modern deep learning models, including working with large-scale, multi-modal foundation models (such as recent LLMs, VLMs).
Strong understanding of deep learning fundamentals and recent trends.

Nice To Haves

Prior work applying human vision science to machine learning and computer vision.
Proficient in crafting, implementing, and conducting behavioral experiments involving human subjects and interactive visual stimuli.
Experience with quantitatively modeling human perception, cognition, movement, and/or decision-making.
Background with multi-node, multi-GPU training and inference workflows.

Responsibilities

Decompose research questions into smaller, more manageable parts and tackle them in iterative steps.
Think critically to identify unseen gaps, and creatively bridge them with non-traditional, high-impact solutions.
Connect with researchers and product engineers to ground research findings in real-world problems.
Lead knowledge dissemination effort, with options for conference, journal, and in-house publication.