Applied Deep Learning Phd Research Intern, Reinforcement Learning for Llms - Fall 2026

NVIDIA NVIDIA · Semiconductors · Santa Clara, CA

PhD research intern focused on advancing LLMs using reinforcement learning. Will design, implement, and evaluate new RL-based methods for improving LLM behavior, reasoning, alignment, and task performance. Involves hands-on experimentation and large-scale GPU cluster work.

What you'd actually do

  1. Develop and prototype reinforcement learning algorithms for large language models
  2. Explore methods for improving reasoning, alignment, instruction following, and multi-turn interaction
  3. Design experiments to evaluate model behavior, robustness, hallucination, and task performance
  4. Implement research ideas in Python and PyTorch, and run experiments on large-scale GPU clusters

Skills

Required

  • PhD student in AI, ML, CS, CE, EE, Math, Physics, or related field
  • Strong background in reinforcement learning
  • Strong background in natural language processing
  • Python programming
  • PyTorch

Nice to have

  • Publications or open-source contributions in RL, LLMs, alignment, reasoning, or post-training
  • Experience with RLHF
  • Experience with RLAIF
  • Experience with policy optimization
  • Experience with reward modeling
  • Experience with agentic LLM systems

What the JD emphasized

  • Pursuing a PhD in AI, ML, CS, CE, EE, Math, Physics, or a related field
  • Strong background in reinforcement learning and natural language processing
  • Excellent programming skills, especially in Python
  • Experience with deep learning frameworks such as PyTorch
  • Comfort with experimental research, debugging models, and working with large-scale training pipelines

Other signals

  • reinforcement learning
  • large language models
  • algorithmic research
  • hands-on experimentation
  • rapid prototyping