Research Internship Reinforcement Learning (Summer)

Responsibilities

• Conduct literature reviews and implement state-of-the-art algorithms in RL and self-distillation.
• Design and execute experiments to evaluate the effectiveness of proposed methods on code generation and agentic tasks.
• Develop and maintain codebases for both theoretical modeling and practical implementations.
• Collaborate with researchers to analyze results, refine methodologies, and prepare findings for publication.
• Contribute to the design of mechanisms for handling large rollouts, such as summarization and hierarchical sub-agents.
• Document progress, methodologies, and outcomes clearly and comprehensively.

Requirements

• Technical Skills:
• Strong background in machine learning, particularly reinforcement learning and deep learning.
• Proficiency in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with LLMs and their training paradigms.
• Experience with coding tasks, unit testing, or compiler tools is a plus.
• Educational Background:
• Currently pursuing a Master’s or PhD in Computer Science, Machine Learning, or a related field.
• Soft Skills:
• Ability to work independently and manage complex projects.
• Strong problem-solving and analytical skills.
• Excellent communication skills for collaborating with a research team.
• Additional:
• Prior experience with RLVR, self-distillation, or large-scale ML experiments is highly desirable.
• Willingness to learn and adapt to new methodologies and tools.

If any of the above doesn’t line up exactly with your experience, we still encourage you to apply

We strive to create an inclusive work environment for all; we welcome applicants from all backgrounds and are committed to providing equal opportunities. Should you require any accommodations during the recruitment process, please submit an Accommodations Request Form, and we will work together to meet your needs.

We may use AI-enabled tools to screen and assess applicants against the criteria for this position. This helps our recruiters identify potentially qualified candidates, but it doesn't limit the applications our recruiters may review or consider.