Sr. AI/ML Engineer III (6273)

MetroStar•Hanscom AFB, MA

About The Position

As a Sr. AI/ML Engineer III , you’ll provide advanced technical expertise in reinforcement learning and applied machine learning to support the development and modernization of mission-critical modeling and simulation capabilities. This role designs, implements, and evaluates reinforcement learning based behaviors and decision-making logic within simulation environments, ensuring that AI-enabled capabilities are technically sound, testable, and operationally relevant. The position is a senior individual contributor role with direct impact on capability modernization and analytical depth across the portfolio. We know that you can’t have great technology services without amazing people. At MetroStar, we are obsessed with our people and have led a two-decade legacy of building the best and brightest teams. Because we know our future relies on our deep understanding and relentless focus on our people, we live by our mission: A passion for our people. Value for our customers. If you think you can see yourself delivering our mission and pursuing our goals with us, then check out the job description below!

Requirements

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.
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.
Support test events, demonstrations, and technical reviews by clearly communicating AI and ML behavior and results.