PhD Research Intern — Efficient Diffusion & Multimodal AI

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

Benefits offered by this job

Intern benefits

Job summary

NVIDIA is seeking an outstanding PhD intern to work on efficient deep learning within the Deep Learning Efficiency Research (DLER) team. The role focuses on diffusion language models, multimodal generation, and efficient, hybrid cloud-edge inference.

You will publish and collaborate across teams, transferring technology to product groups and external researchers, with a strong emphasis on real-world impact.

Qualifications

• Pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc.
• Excellent knowledge of theory and practice of machine learning and deep learning.
• Experience with large language models, diffusion language models, multimodal/vision-language models, or agentic systems is required.
• Hands-on experience with large-scale model training including data preparation and model parallelization (tensor and pipeline).
• Outstanding research track record with at least one top-tier conference (ICML, ICLR, NeurIPS, CVPR, ICCV, etc.).
• Excellent communication skills.

Responsibilities

• Research, design, and implement novel methods for efficient deep learning in one or both of the team’s focus areas: diffusion LLMs and multimodal models; efficient agentic AI with hybrid inference across cloud and edge.
• Publish original research.
• Collaborate with other team members and teams.
• Work with product groups to transfer technology.
• Collaborate with external researchers.

Skills

Machine learning

Deep learning

Research experience

Communication skills

Education

PhD candidate in CS/Engineering/EE

Tools

CUDA

Job description

NVIDIA is seeking an outstanding PhD intern to work on efficient deep learning within the Deep Learning Efficiency Research (DLER) team. The role focuses on diffusion language models, multimodal generation, and efficient, hybrid cloud-edge inference.

You will publish and collaborate across teams, transferring technology to product groups and external researchers, with a strong emphasis on real-world impact.