Radar Modeling Simulation Engineer Job Listing at SAIC in Chantilly, VA (Job ID 2612853)

Description

Description

SAIC has a funded need for a Radar Modeling 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.

This position offers a flexible work schedule, with core collaboration hours from 10:00 a.m. – 3:00 p.m., allowing you to balance mission demands with personal flexibility while still engaging deeply with a world‑class engineering team.

Clearance Requirements: Current and Active Top Secret/SCI with the ability to obtain a Poly.  (Note: The current Security Clearance must be held by an IC Customer.) 

Role Overview

As a senior technical contributor, you will design, develop, and refine high‑fidelity radar and RF system models that underpin mission architecture decisions across the NRO's space enterprise. Your modeling and simulation products will inform spacecraft design, sensor performance, mission planning, and system‑of‑systems behavior—ultimately shaping how future space‑domain awareness and intelligence missions are executed.

You will work at the intersection of physics‑based modeling, orbital mechanics, sensor phenomenology, and digital signal processing, producing insights that guide engineering trades, reduce technical risk, and strengthen mission assurance. Your analyses will be used by senior Government leaders, system architects, and program managers to evaluate design options, validate performance, and chart the path for next‑generation space capabilities.

Key Responsibilities to include:

• Develop advanced RF simulations — Build, refine, and maintain physics‑based radar and RF system models that characterize spacecraft sensor performance, signal behavior, and space‑domain awareness capabilities.
• Perform end‑to‑end mission modeling — Integrate orbital dynamics, environmental effects, sensor models, and system‑level interactions to evaluate mission performance across full operational scenarios.
• Analyze radar and RF data — Conduct RF phenomenology modeling, radar sensing analysis, and digital signal processing to assess detection, tracking, and characterization performance.
• Automate modeling workflows — Use Python, MATLAB, or similar tools to automate simulation pipelines, streamline data analysis, and interface between modeling environments for repeatable, high‑quality results.
• Produce technical artifacts — Generate clear, defensible technical documentation, simulation reports, and visualizations that support engineering decisions, design reviews, and customer briefings.
• Support design reviews & trade studies — Provide modeling‑driven insights that quantify performance impacts, evaluate design alternatives, and inform system‑level tradeoffs across spacecraft and mission architectures.

Qualifications

Required Education and Experience

• Bachelor's degree in Electrical Engineering, Physics, or a related field with 9+ years of professional technical experience (Master's degree with 7+ years, or PhD with 4+)
• Current and Active Top Secret/SCI with the ability to obtain a Poly.  (Note: The current Security Clearance must be held by an IC Customer.) 
• Radar & RF modeling experience — 4+ years of system‑level modeling and simulation for radar or communications systems on space vehicles (preferred).
• High‑fidelity radar modeling — Experience developing or evaluating high‑fidelity radar models for space‑domain awareness or related mission areas.
• Sensor phenomenology expertise — Strong understanding of RF and radar phenomenology, electromagnetic propagation, and sensor‑environment interactions.
• Mathematics & estimation — Solid foundation in applied mathematics, control systems, and state estimation techniques used in modeling and simulation.
• Programming & analysis — Intermediate Python experience preferred; familiarity with NumPy, SciPy, pandas, and matplotlib is beneficial for building efficient analysis pipelines.
• Clear technical communication — Ability to translate complex modeling results into clear, actionable insights for both technical and non‑technical audiences, including senior Government leadership.