Machine Learning Modeling and Simulation Engineer

SAIC (Science Applications Int.)•Chantilly, VA

70d

About The Position

SAIC is seeking a Machine Learning Modeling and Simulation Engineer in Chantilly, VA. The successful candidate will: 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.

Requirements

Bachelor's or Master's degree in Aerospace Engineering, Mechanical Engineering, Physics, or a related field with 5+ years of professional technical experience
3+ years of experience in modeling and simulation for aerospace or space systems.
Active Top Secret/SCI w/Poly Clearance
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.

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.