MetroStar is looking to modernize mission-critical modeling and simulation capabilities by providing advanced technical expertise in reinforcement learning and applied machine learning.
Requirements
- Demonstrated hands-on experience implementing and tuning reinforcement learning algorithms in Python.
- Experience with reinforcement learning approaches such as policy gradient methods, Q-learning, and actor-critic models.
- Proven ability to integrate ML or RL components into larger software or modeling and simulation systems.
- Experience working closely with software engineers and modeling teams to operationalize AI solutions.
Responsibilities
- 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.
Other
- Hold an active U.S. Government issued Secret clearance.
- 10+ years of experience in data science with some of those years focused on artificial intelligence, or applied machine learning.
- Support test events, demonstrations, and technical reviews by clearly communicating AI and ML behavior and results.
- In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.
