Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs

Nvidia

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Work type
On-site
Location
Santa Clara, CA
Employment
Full-time
Posted
15 days ago
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Confirmed live today
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TL;DR · Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs

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JOB TITLE: Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026 The Applied Deep Learning research team is seeking a PhD research intern to advance the next generation of large language models through reinforcement learning. You will design, implement, and evaluate new RL-based methods to improve model reasoning, alignment, instruction following, and multi-turn interaction. Your daily work involves prototyping algorithms, designing experiments to assess robustness and hallucination, and running large-scale training pipelines. You will utilize Python and PyTorch to implement research ideas on large-scale GPU clusters. The role requires a strong background in reinforcement learning and natural language processing. You will solve technical problems related to post-training, policy optimization, reward modeling, and agentic LLM systems to make models more capable, reliable, and useful for solving real-world tasks.

Skills

Reinforcement Learning Large Language Models Python PyTorch Deep Learning Natural Language Processing RLHF RLAIF Policy Optimization Reward Modeling GPU Clusters Large-scale Training Pipelines Algorithmic Research

What you'll do

  • Develop and prototype reinforcement learning algorithms for large language models.
  • Explore methods to improve model reasoning, alignment, and instruction following.
  • Design experiments to evaluate model robustness, hallucinations, and task performance.
  • Implement research ideas using Python and PyTorch.
  • Run experiments on large-scale GPU clusters.
  • Conduct rapid prototyping and hands-on experimentation at scale.

What we're looking for

  • 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.
  • Publications or open-source contributions in RL, LLMs, alignment, reasoning, or post-training (preferred).
  • Experience with RLHF, RLAIF, policy optimization, reward modeling, or agentic LLM systems (preferred).
  • Strong intuition for both algorithms and large-scale implementation (preferred).

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