[Early Careers] Reinforcement Learning Intern

Design, train, and evaluate reinforcement learning agents using frameworks such as Gym or Unreal engine.

Implement and test reward functions, policy optimization techniques, and training pipelines.

Conduct experiments to measure agent performance and learning efficiency.

Collaborate with mentors to refine models and interpret experimental data.

Document processes, findings, and insights.

Gain hands-on understanding of reinforcement learning algorithms (Q-learning, PPO, DQN, etc.).

Learn to design training environments, rewards, and evaluation metrics.

Build practical skills in debugging, experiment tracking, and model improvement.

Develop the ability to connect theoretical RL concepts with real-world AI applications.