• Design, implement, and tune reinforcement learning algorithms to support decision making and behavior modeling in simulation environments. • Define and refine reward functions, state representations, and action spaces aligned to mission objectives. • Build and maintain training environments and experimentation pipelines for reinforcement learning agents. • Evaluate agent performance using rigorous metrics such as convergence, sample efficiency, robustness, and stability. • Analyze training outcomes and simulation results to recommend changes to models, scenarios, or learning approaches. • Collaborate closely with modeling and simulation engineers and software teams to integrate RL components into the baseline. • Ensure AI and ML components are transparent, testable, and maintainable within the broader system architecture. • Support test events, demonstrations, and technical reviews by clearly communicating AI and ML behavior and results.