RL AI Research Intern

Role overview A 12 to 16 week RL AI Research Internship embedded directly with a research team working on reinforcement learning for AI agents. The role is hands-on, involving experiment design, algorithm implementation, and meaningful contributions to core technology, with mentorship from experienced researchers. The internship is remote, with a preference for candidates located in the US or Singapore.

Responsibilities

- Investigate and prototype novel RL approaches for context selection, reward shaping, or policy optimization in agent workflows. - Design and execute experiments, producing clear analyses and actionable insights. - Implement and benchmark RL algorithms against internal and public baselines. - Collaborate with senior researchers and engineers in a fast-paced startup environment. - Document findings and present results to the team, with the possibility of contributing to publications.

Requirements

- Currently pursuing a PhD or Master's degree in Reinforcement Learning, Machine Learning, Computer Science, or a related field. - Solid understanding of RL fundamentals, including MDPs, policy gradients, Q-learning, and actor-critic methods. - Strong programming skills in Python with experience in PyTorch or similar frameworks. - Ability to independently design experiments and iterate quickly on ideas. - Excellent written and verbal communication skills.

Nice to have

- Publications or preprints at top machine learning venues. - Experience with LLM fine-tuning, RLHF, or AI agent frameworks. - Familiarity with distributed training or large-scale experiment infrastructure. - Prior industry research internship experience.