Machine Learning Modeling and Simulation Engineer

SAIC•Chantilly, VA

18h•Hybrid

About The Position

As the Machine Learning Modeling and Simulation Engineer, you will provide technical expertise across a variety of Machine Learning (ML) and Modeling and Simulation (M&S) topics, including developing and training ML models, designing simulation frameworks, conducting performance analyses, and applying data-driven approaches to solve complex problems. You will also assist with Systems Engineering topics (e.g., requirements, configuration management, readiness, verification and validation, etc.) to ensure seamless integration of ML capabilities within simulation environments. This role supports a rapidly expanding Government Intelligence Community (IC) customer with cutting-edge programs within the National Reconnaissance Office (NRO) in Chantilly, VA. The role offers a flexible work schedule, but requires availability for team meetings during core business hours (10:00 a.m. – 3:00 p.m.).

Requirements

Technical expertise across a variety of Machine Learning (ML) and Modeling and Simulation (M&S) topics.
Experience developing and training ML models.
Experience designing simulation frameworks.
Ability to conduct performance analyses.
Ability to apply data-driven approaches to solve complex problems.
Familiarity with Systems Engineering topics (e.g., requirements, configuration management, readiness, verification and validation).
Proficiency in scripting languages (Python, MATLAB, or similar).
Ability to apply Artificial Intelligence/Machine Learning (AI/ML) techniques (e.g., supervised/unsupervised learning, reinforcement learning, predictive modeling).
Proficiency in statistical and machine learning techniques for data analysis.

Responsibilities

Develop and maintain physics-based simulation models of spacecraft systems, including structures, sensors, and mission environments.
Perform end-to-end performance modeling for satellite missions, integrating sensor, orbital, and environmental models.
Conduct sensor phenomenology studies, including optical, infrared, or radar modeling for detection, tracking, and signature analysis.
Perform orbital mechanics modeling including orbit determination, orbital maneuvering, and spacecraft flight dynamics.
Use scripting languages (Python, MATLAB, or similar) to automate workflows, perform data analysis, and interface between simulation tools.
Apply Artificial Intelligence/Machine Learning (AI/ML) techniques (e.g., supervised/unsupervised learning, reinforcement learning, predictive modeling) to enhance simulation fidelity and performance.
Develop AI/ML models to analyze and predict satellite system behaviors, performance metrics, and mission outcomes based on simulation data.
Design and implement algorithms for anomaly detection, predictive maintenance, and optimization of satellite operations.
Use statistical and machine learning techniques to analyze data, identify patterns, and uncover insights relevant to satellite systems.
Integrate AI/ML models into existing simulation frameworks and tools to enhance their capabilities.
Provide value-added judgment and offer strategic recommendations to the customer on program objectives, advanced technologies, and system enhancements.
Produce highly detailed, practical, and consistent deliverables that align with the organization’s mission and objectives, with a focus on innovation and cutting-edge solutions in machine learning and simulation.