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About the Role
We are seeking a highly motivated intern to join our research team working on self-improving agentic systems. This cutting-edge area combines reinforcement learning, LLM post-training, and autonomous AI research agents. Our work focuses on developing agentic systems that improve their own performance over time, 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.
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.
Requirements
- Currently 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.
Skills
- Python
- C++
- C
- Java
- Pytorch
- Tensorflow
- Machine learning
- Deep learning
- Reinforcement learning
- LLM post-training
- Agentic scaffolding
- Self-improvement
- Problem-solving
- Communication
Work Type
- Contract Worker
Experience Level
- Intern
Education Level
- Ph.D. degree in Machine Learning, Artificial Intelligence, Computer Science, Information or Multimedia Retrieval, Reinforcement Learning, Mathematics or relevant technical field