Overview
On Site
Full Time
Skills
Science
SIM
Astrodynamics
Dynamics
Scripting
MATLAB
Workflow
Data Analysis
Management
Unsupervised Learning
Predictive Modelling
Performance Metrics
Algorithms
Optimization
Satellite
Artificial Intelligence
Machine Learning (ML)
Mechanical Engineering
Physics
Aerospace
Security Clearance
Sensors
Modeling
Data Manipulation
Python
NumPy
Pandas
matplotlib
Information Technology
Systems Engineering
FOCUS
Job Details
Job ID: 2510926
Location: CHANTILLY, VA, US
Date Posted: 2025-10-21
Category: Engineering and Sciences
Subcategory: Modeling/Sim Engr
Schedule: Full-time
Shift: Day Job
Travel: No
Minimum Clearance Required: TS/SCI with Poly
Clearance Level Must Be Able to Obtain: None
Potential for Remote Work: No
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.
Location: CHANTILLY, VA, US
Date Posted: 2025-10-21
Category: Engineering and Sciences
Subcategory: Modeling/Sim Engr
Schedule: Full-time
Shift: Day Job
Travel: No
Minimum Clearance Required: TS/SCI with Poly
Clearance Level Must Be Able to Obtain: None
Potential for Remote Work: No
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.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.