Research Intern, Fundamental Generative AI - 2027

at Nvidia

USD 38-94 per hour

INTERN

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 7 CUDA @ 3 Communication @ 3 Computer Vision @ 6 Deep Learning @ 3 GenAI Generative AI @ 7 Machine Learning @ 6 NLP Parallel Programming @ 3 PyTorch @ 3 Python @ 3 Statistics

Details

NVIDIA Research is seeking a generative AI intern to join the Fundamental Generative AI research team. The team develops next-generation generative models with real-world impact, focusing on image, video, 3D, graph, and multimodal data, as well as generative AI applications in structural biology, chemistry, drug discovery, climate modeling, and weather forecasting. The intern will work in a collaborative research environment that publishes at leading machine learning venues.

Responsibilities

Research, design, and implement novel large-scale generative AI methods.
Publish original research.
Collaborate with team members, research teams, and product teams.
Transfer technology to product groups.

Requirements

Pursuing a Ph.D. in Computer Science, Computer Engineering, Statistics, Electrical Engineering, or a related field.
Strong publication record at leading machine learning venues such as NeurIPS, CVPR, ICLR, ICML, ICCV, and ECCV.
Excellent knowledge of the theory and practice of generative AI.
Strong knowledge of computer vision, 3D, natural language processing, or AI for science.
Excellent programming skills in rapid prototyping environments such as Python.
Knowledge of common deep learning frameworks, such as PyTorch.
Excellent communication skills.
C++ and parallel programming, such as CUDA, are a plus.