Research Intern - Self-Improving Agentic Systems and AI Research Agents

We develop agentic systems that improve their own performance over time, combining reinforcement learning and LLM post-training with agents capable of writing, evaluating, and optimizing code. A central goal is building AI research agents that can autonomously propose, run, and analyze experiments, accelerating the pace of AI research itself.

Responsibilities

Assist in the design, implementation, and evaluation of self-improving agentic systems and AI research agents. Contribute to research on reinforcement learning and LLM post-training methods, including reward design, preference optimization, and RL from execution feedback. Collaborate with senior researchers to explore new approaches for autonomous experimentation, code generation, and optimization. Help build and maintain evaluation harnesses and benchmarks for agentic capabilities. Help document findings and prepare research outputs for publication.

Qualifications

Currently is in the process of obtaining a Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics or relevant technical field Experience with Python, C++, C, Java or other related language Experience with deep learning frameworks such as Pytorch or Tensorflow Experience building systems based on machine learning and/or deep learning methods Eagerness to learn about advanced AI concepts such as reinforcement learning, LLM post-training, agentic scaffolding, and self-improvement Excellent problem-solving and communication skills