NVIDIA
PhD Research Intern, Efficient Deep Learning - 2027
US, CA, Santa Clara +1 · Internship · Posted Oct 12, 2026
- Location
- US, CA, Santa Clara
US, CA, Remote - Education
- Pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc.
About the job
Conduct and publish research on methods that make deep learning and generative or agentic AI systems more efficient, and help transfer the resulting technology to products.
What you'll do
- Research, design, and implement efficient deep learning methods for diffusion and multimodal models or agentic AI.
- Publish original research and collaborate with internal teams and external researchers.
- Work with product groups to transfer technology.
What they ask for
- Pursuing a Ph.D. in Computer Science/Engineering, Electrical Engineering, etc.
- Excellent knowledge of machine learning and deep learning theory and practice.
- Experience with large language models, diffusion language models, multimodal / vision-language models, or agentic systems.
- Hands-on experience with large-scale model training, including data preparation and tensor and pipeline model parallelization; at least one top-tier conference publication; excellent communication skills.
Nice to have
- Parallel programming (e.g., CUDA).
- Interest or experience in hybrid cloud–edge inference, orchestration, or adaptive routing; background in pruning, quantization, NAS, or efficient backbones.
Pay
38 USD - 94 USD hourly rate
Tools
CUDA
AI focus
Research on efficient diffusion language and multimodal models, agentic AI, hybrid cloud–edge inference, model optimization, architecture design, adaptive inference, and resource-efficient training and finetuning.
Model-written summary · the original posting is authoritative