Today
Top Secret/SCI
Unspecified
Polygraph
Engineering - Systems
Chantilly, VA (On-Site/Office)
Description
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.
Qualifications
• 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.
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.
Qualifications
• 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.
group id: 10111346
