NVIDIA

AppliedDeepLearningPhDResearchIntern,ReinforcementLearningforLLMs-Fall2026

$0–0k Santa Clara, California, United States FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026 at NVIDIA. Skills: Reinforcement Learning, Large Language Models, Deep Learning, Python, PyTorch. Develop and prototype reinforcement learning algorithms for large language models. Explore methods for improving reasoning, alignment, instruction following, and multi-turn interaction”

What You'll Achieve.

Improve LLM behavior; Improve reasoning; Improve alignment; Improve instruction following; Improve multi-turn interaction; Evaluate model behavior; Evaluate model robustness; Evaluate model hallucination; Evaluate task performance

Industry & Context.

Problems you'll solve

Improve LLM behavior; Improve reasoning; Improve alignment; Improve instruction following; Improve multi-turn interaction; Evaluate model behavior; Evaluate model robustness; Evaluate model hallucination; Evaluate task performance

What They're Looking For.

Must Have

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

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, intuition for both algorithms and large-scale implementation

What You'll Do.

Develop and prototype reinforcement learning algorithms for large language models

Explore methods for improving reasoning

instruction following

and multi-turn interaction

Design experiments to evaluate model behavior

Implement research ideas in Python and PyTorch

Run experiments on large-scale GPU clusters

Full Job Description

We are looking for PhD research interns excited to advance the next generation of large language models through reinforcement learning. Our applied deep learning research team at NVIDIA has helped pioneer projects such as Megatron, MT-NLG, and DLSS. We build state-of-the-art foundation models and develop new methods to improve their reasoning, alignment, reliability, and ability to solve real-world tasks. This internship will focus on algorithmic research at the intersection of reinforcement learning and large language models. You will design, implement, and evaluate new RL-based methods for improving LLM behavior, with a strong emphasis on hands-on experimentation and rapid prototyping at scale. **What you will be doing:** * Develop and prototype reinforcement learning algorithms for large language models * Explore methods for improving reasoning, alignment, instruction following, and multi-turn interaction * Design experiments to evaluate model behavior, robustness, hallucination, and task performance * Implement research ideas in Python and PyTorch, and run experiments on large-scale GPU clusters **What we need to see:** * 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 **Ways to stand out from the crowd:** * Publications or open-source contributions in RL, LLMs, alignment, reasoning, or post-training * Experience with RLHF, RLAIF, policy optimization, reward modeling, or agentic LLM systems * Strong intuition for both algorithms and large-scale implementation If you are excited about using reinforcement learning to make language models more capable, reliable, and useful, this team could be a great fit. Our internship hourly rates are a standard pay b

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