RL AI Research Intern

Pokee AI; United States; Remote; USD 20,000 - 33,000; Full time

Benefits offered by this job

Publications or preprints

Remote internship

Mentorship from researchers

Job summary

Pokee AI is offering a 12 to 16 week RL AI Research Internship embedded with a research team focused on reinforcement learning for AI agents. The role is hands-on, with experiment design, algorithm implementation, and mentorship from experienced researchers.

This remote internship has a preference for candidates located in the US or Singapore. Responsibilities include designing experiments, implementing RL algorithms, benchmarking against baselines, and communicating findings.

Qualifications

• Currently pursuing a PhD or Master’s in RL, ML, CS, or related field.
• Solid understanding of RL fundamentals: MDPs, policy gradients, Q-learning, actor-critic methods.
• Strong Python programming with PyTorch or similar frameworks.
• Ability to independently design experiments and iterate quickly.
• Excellent written and verbal communication skills.

Responsibilities

• Design and prototype novel RL approaches for context selection, reward shaping, or policy optimization.
• Design and execute experiments and deliver clear analyses.
• Implement and benchmark RL algorithms against baselines.
• Collaborate with senior researchers and engineers in a startup environment.
• Document findings and present results; possible publications.

Skills

Experiment design

RL fundamentals

Python programming

Communication

Education

PhD or Master’s in RL/ML/CS

Tools

PyTorch

Job description

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.