Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs - Fall 2026

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

Qualifications

• 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

Skills

• Reinforcement Learning
• Natural Language Processing
• Python Programming
• Deep Learning Frameworks (PyTorch)

Benefits

You will be eligible for Intern benefits.

Pay

The hourly rate for our interns is 30 USD - 94 USD.

Schedule

Applications for this job will be accepted at least until May 10, 2026.