Applied Deep Learning PhD Research Intern, Reinforcement Learning for LLMs

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 has helped pioneer projects such as Megatron, MT-NLG, and DLSS. 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 behaviour, with a strong emphasis on hands-on experimentation and rapid prototyping at scale.

Responsibilities:

• 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 behaviour, robustness, hallucination, and task performance
• Implement research ideas in Python and PyTorch, and run experiments on large-scale GPU clusters

Requirements:

• 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