Remote RL AI Research Intern: Experiment & Prototype

Pokee AI; USA; Remote; USD 20.000 - 33.000; Vollzeit

Benefits dieser Stelle

Publications or preprints

Remote internship

Mentorship from researchers

Zusammenfassung

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.

Qualifikationen

• 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.

Aufgaben

• 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.

Kenntnisse

Experiment design

RL fundamentals

Python programming

Communication

Ausbildung

PhD or Master’s in RL/ML/CS

Tools

PyTorch

Jobbeschreibung

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.