Modeling & Simulation Engineer, CHAOS Industries

• This role requires strong technical judgment, software ability, and engineering fundamentals
• Experience in every area below is not expected; candidates with depth in one or more relevant modeling and simulation domains are encouraged to apply
• Bachelor’s degree in engineering, computer science, physics, applied mathematics, or a related technical field, or equivalent demonstrated expertise
• 5+ years of experience developing, integrating, validating, or using models and simulations for complex technical systems
• Strong programming ability in one or more relevant languages or environments such as Python, C++, C, Rust, MATLAB/Simulink, or similar
• Strong fundamentals in engineering, physics, mathematics, numerical methods, software development, or data analysis
• Experience analyzing simulation outputs, test data, telemetry, sensor data, or engineering data sets to support technical decisions
• Experience with modern software development practices such as Git, code review, automated testing, issue tracking, CI/CD, and reproducible workflows
• Ability to work independently, make sound technical judgments, document assumptions clearly, and collaborate across engineering disciplines
• Eligibility to obtain and maintain a Top Secret / Sensitive Compartmented Information clearance

Desirable

• As a Modeling & Simulation Engineer, you will develop and maintain simulations for complex systems and subsystems across vehicle, sensor, software, and hardware domains
• You will work with engineering teams to select the right model fidelity, build credible simulation environments, analyze results, and validate models against test data
• The team owns simulation capabilities across CHAOS’s product portfolio, from fast-running engineering models to high-fidelity closed-loop simulations, with model fidelity matched to the decision, test event, or verification objective
• You will help shape simulation architecture, model interfaces, validation workflows, and the tools used to support design trades, integration, test planning, and system verification
• Develop, integrate, and maintain models and simulations for advanced defense systems, including sensors, vehicles, effectors, environments, autonomy software, embedded software, and hardware interfaces
• Build system- and subsystem-level simulations to support design trades, performance assessment, integration, test planning, verification, field-test preparation, and post-test analysis
• Develop models of RF/radar, EO/IR, communications, seekers, navigation sensors, vehicles, actuators, environments, targets, and other system elements at appropriate levels of fidelity
• Support simulation environments such as software-in-the-loop (SITL), hardware-in-the-loop (HITL), hardware/software integration labs (HIL/SIL), real-time simulation, discrete-event simulation, automated regression testing, Monte Carlo studies, and large-scale batch analysis
• Work with RF, EO/IR, sensors, GNC, autonomy, flight software, embedded software, systems engineering, test, and product teams to define simulation requirements, interfaces, assumptions, and validation criteria
• Run sensitivity, uncertainty, design-of-experiments, and performance studies to characterize system behavior and inform engineering decisions
• Validate and correlate models against analytical references, lab data, calibration data, ground test, flight test, range data, hardware test, field data, or other authoritative sources
• Develop and maintain simulation software, model APIs, configuration systems, scenario definitions, data pipelines, analysis tools, visualization workflows, and automated validation tests
• Integrate embedded software, autonomy software, flight software, sensor models, hardware models, or subsystem models into closed-loop simulation environments
• Produce clear technical documentation, model assumptions, validation evidence, analysis summaries, engineering-review material, customer briefings, and CDRL-supporting content
• Help establish best practices for model development, simulation credibility, software quality, version control, reproducibility, model review, and regression testing