Modeling & Simulation Engineer, CHAOS Industries

• Experience in every area below is not expected; candidates with depth in one or more relevant modeling and simulation domains are encouraged to apply
• Signal- or complex-IQ-level radar simulation
• Radar detection, measurement generation, discrimination, or signal processing
• Radar tracking, estimation, data association, or sensor fusion
• Radar modes, resource management, or system-performance analysis
• RF propagation, clutter, interference, electronic warfare, or electronic protection
• Real-time radar stimulation, HITL, or HWIL
• Strong programming ability in at least one scientific or analytical environment such as MATLAB, Python, or C++
• Experience with Monte Carlo simulation, sensitivity analysis, statistical analysis, uncertainty modeling, or design of experiments
• Experience validating or correlating models against analytical results, laboratory measurements, recorded sensor data, range data, or field or flight-test data
• Experience analyzing large engineering data sets and communicating technically defensible conclusions
• Experience with Git, code review, automated testing, issue tracking, continuous integration, and reproducible software or analysis workflows
• Ability to work independently, make sound technical judgments, document assumptions and limitations clearly, and collaborate across engineering disciplines
• Active Secret clearance

Desirable

• The Modeling & Simulation team builds the simulation capabilities used to design, integrate, test, and validate advanced aerospace and defense systems. We develop physics-based models, simulation environments, and analysis tools that help engineering teams evaluate system behavior, improve designs, and reduce risk before hardware design, integration, test, and fielding
• As a Modeling & Simulation Engineer for Radars, you will develop, integrate, and validate models of radar and RF sensing systems. You will work across analytical, behavioral, measurement-level, signal/IQ-level, and closed-loop system models, selecting the fidelity appropriate to the design decision, software integration activity, test event, or verification objective. You will partner with radar architecture, RF and antenna, signal processing, tracking, embedded software, systems engineering, test, and product teams
• Develop, integrate, and maintain physics-based and performance-based models of radar and RF sensing systems
• Build models at appropriate levels of fidelity, including analytical radar-performance models, behavioral sensor models, measurement-generation models, signal or complex-IQ models, radar-mode models, tracker models, and closed-loop system simulations
• Model applicable radar effects such as antenna and beam patterns, beam steering, waveforms, RF-chain behavior, thermal noise, propagation loss, atmospheric effects, multipath, clutter, interference, jamming, target radar cross section, calibration errors, measurement biases, and uncertainty
• Develop or integrate applicable detection, thresholding, discrimination, measurement extraction, data-association, tracking, estimation, classification, sensor-fusion, radar-mode, and resource-management functions
• Build target, scene, geometry, trajectory, environmental, and threat models needed to evaluate radar performance in operationally relevant scenarios
• Perform design trades, sensitivity studies, Monte Carlo analysis, uncertainty quantification, and design of experiments
• Characterize performance using metrics such as signal-to-noise or signal-to-interference-plus-noise ratio, probability of detection, false-alarm performance, coverage, detection range, measurement accuracy, track accuracy, revisit rate, update rate, latency, track capacity, and robustness to environmental or threat effects
• Integrate radar algorithms, radar software, embedded software, sensor models, target models, and hardware interfaces into SIL, SITL, HITL, HWIL, real-time, or large-scale batch simulation environments as applicable
• Validate and correlate models against analytical references, laboratory and chamber measurements, calibration data, recorded signal or IQ data, radar-range data, field-test data, flight-test data, and other authoritative sources
• Define model requirements, interfaces, assumptions, limitations, validity domains, configuration controls, and acceptance or credibility criteria
• Develop and maintain simulation software, model APIs, scenario-generation tools, configuration systems, data pipelines, visualization tools, analysis workflows, and automated regression tests
• Support pre-test prediction, test planning, test-card or scenario development, test execution, anomaly investigation, and post-test analysis
• Produce clear model documentation, validation evidence, performance reports, engineering-review material, and customer or program deliverables