Location
London

Hours
Full Time (hours to be confirmed)

Salary
Negotiable

About the Role
We are seeking a highly motivated intern (Contract Worker) to join our research team working on self-improving agentic systems — a cutting-edge area at the intersection of reinforcement learning, LLM post-training, and autonomous AI research agents. You will assist in the design, implementation, and evaluation of agentic systems that improve their own performance over time by combining reinforcement learning and LLM post-training with agents capable of writing, evaluating, and optimizing code. A central goal is to build AI research agents that can autonomously propose, run, and analyze experiments, accelerating the pace of AI research itself. Responsibilities include contributing to research on reinforcement learning and LLM post-training methods such as reward design, preference optimization, and RL from execution feedback; collaborating with senior researchers to explore new approaches for autonomous experimentation, code generation, and optimization; building and maintaining evaluation harnesses and benchmarks for agentic capabilities; and documenting findings to prepare research outputs for publication.

Experience
Currently pursuing a Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics, or a relevant technical field. Experience with Python, C++, C, Java, or other related programming languages. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience building systems based on machine learning and/or deep learning methods.

About you
Eagerness to learn advanced AI concepts including reinforcement learning, LLM post-training, agentic scaffolding, and self-improvement. Excellent problem-solving and communication skills. Motivated to work in a fast-paced research environment and collaborate effectively with senior researchers.

Qualifications
Currently enrolled in a relevant Ph.D. program or equivalent advanced degree studies in a related technical field.