NVIDIA 2026 Internships: PhD Large Language Models Research

By submitting your resume, you’re expressing interest in one of our 2026 Large Language Models focused Research Internships. We’ll review resumes on an ongoing basis, and a recruiter may reach out if your experience fits one of our many internship opportunities. 

NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society — from gaming to robotics, self-driving cars to life-saving healthcare, climate change to virtual worlds where we can all connect and create. 

Our internships offer an excellent opportunity to expand your career and get hands on experience with one of our industry leading LLM teams. We’re seeking strategic, ambitious, hard-working, and creative individuals who are passionate about helping us tackle challenges no one else can solve. 

Learn more about Research at NVIDIA. 

What you will be doing: 

• Research and develop novel methods for advancing the capabilities of large language and multimodal models. 

• Collaborate with other team members, teams, and/or external researchers. 

• Transfer your research to product groups to enable new products or types of products. Deliverable results include prototypes, patents, products, and/or publishing original research. 

What we need to see: 

• Must be actively enrolled in a university pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field, for the entire duration of the internship.     

• Depending on the internship, prior experience or knowledge requirements could include the following programming skills and technologies:  

• Python, C++, CUDA, Deep Learning Framworks (PyTorch, Tensorflow, JAX, etc.) 

• Strong background in research with publications at top conferences. 

• Excellent communication and collaboration skills. 

• Experience with large-scale model training is a plus. 

Potential internships require research experience in at least one of the following areas: 

Large Language Models and Foundation Models 

• Transformer architectures 

• Knowledge distillation and data synthesis 

• Long-context methods 

Model Efficiency and Optimization 

• Model compression and pruning 

• Quantization 

• Inference optimization and acceleration 

• Parameter-efficient fine-tuning 

• Neural Architecture Search (NAS) 

Training and Alignment 

• Large-scale model training 

• Instruction tuning 

• Reinforcement Learning 

• Advanced reasoning and test-time inference 

• Few-shot and zero-shot learning 

Synthethic data generation 

Multimodal and Vision Language Models 

Retrieval-Augmented Generation (RAG) 

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