Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026
At a glance
Junior AI Research role at NVIDIA. US, CA, Santa Clara · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and test reinforcement-learning approaches intended to change how large language models behave
- Investigate ways to improve models’ reasoning, alignment, instruction following and multi-turn exchanges
- Set up evaluations of model behavior, robustness, hallucinations and performance on tasks
- Turn research ideas into Python and PyTorch implementations, then run trials on large GPU clusters
What you bring
- You are working toward a PhD in AI, machine learning, computer science, engineering, math, physics or a related area
- You bring strong knowledge of reinforcement learning and natural language processing
- You have excellent programming ability, especially with Python, and experience using a deep-learning framework such as PyTorch
- You are comfortable conducting experiments, debugging models and using large-scale training pipelines
Who this fits
This internship suits a PhD researcher looking for hands-on algorithm development and large-scale experimentation in reinforcement learning for language models. The role is an individual research internship focused on prototyping and evaluating methods.
From the employer
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 based on the position, your location, year in school, degree, and experience. The hourly rate for our interns is 38 USD - 94 USD.You will also be eligible for Intern benefits.
Applications for this job will be accepted at least until October 16, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.