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Job ID: 2611773

Location: Chantilly, VA, US

Date Posted: 2026-04-22

Category: Engineering and Sciences

Subcategory: Modeling/Sim Engr

Schedule: Full-Time

Shift: Day Job

Travel: No

Minimum Clearance Required: TS.SCI_wPoly

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_ON_SITE

Description

SAIC has need for a Machine Learning Modeling and Simulation Engineer  to support a rapidly expanding Government Intelligence Community (IC) customer with cutting-edge programs within the National Reconnaissance Office (NRO) in Chantilly, VA.

Note:  The role offers a flexible work schedule, but we ask our team to be available for team meetings during core business hours (10:00 a.m. – 3:00 p.m.).

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. 

Job Duties to include:

• 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. 

Qualifications

Required Education and Experience:

• Bachelor's Aerospace Engineering, Mechanical Engineering, Physics, and five (5) years or more experience; Masters and three (3) years or more experience; PhD and 0 years related experience. 
• Active Top Secret/SCI w/Poly Clearance.
• 3+ years of experience in modeling and simulation for aerospace or space systems.
• Strong understanding of sensor phenomenology --such as optical, infrared, or radar systems --and associated modeling methods.
• Intermediate Python programming experience, demonstrated through hands-on experience with tasks such as data manipulation, automation, and development of Python-based solutions. Experience with libraries such as NumPy, SciPy, pandas, and matplotlib is beneficial.
• Ability to communicate technical results clearly in written and verbal formats.