Staff System Modeling Engineer (Warfighter Systems)
Anduril Industries
Requirements The ideal candidate will leverage first-principles physics, empirical data, and advanced mathematical techniques to create high-fidelity, predictive models used across the entire product lifecycle – from concept exploration and design optimization to control system development and virtual testing 9+ years of experience in system modeling, simulation, and analysis, particularly with electro-mechanical systems Demonstrated experience in developing models for at least two of the following: Sensors: IMUs (accelerometers, gyros), Lidars, Radars, Ultrasonic sensors, microphones, etc. (noise characteristics, drift, latency, sensor fusion) Cameras: Optical models, image processing pipeline models, distortion, noise, dynamic range, rolling shutter effects Display: See/pass-through near eye display architectures, optical modeling, validations, & human perception Strong theoretical foundation in classical and modern control systems, dynamics, signal processing, and numerical methods Proficiency in primary modeling and simulation tools such as MATLAB/Simulink (Simscape a plus), Python (SciPy, NumPy, Pandas), or C/C++ Experience with model validation against experimental data and statistical analysis techniques Excellent problem-solving skills, with an ability to apply first principles to complex engineering challenges Strong communication and interpersonal skills, with the ability to work effectively in a team environment. Strong technical communication skills to collaborate effectively with cross-functional teams U.S. Person Status: Due to government contract requirements, candidates must be a U.S. Person (U.S. Citizen or Green Card holder) Bachelor's or Master's degree in Electrical Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, Physics, or a related quantitative field
- Desirable) Ph.D. in a relevant engineering or scientific discipline
- Desirable) Familiarity with Hardware-in-the-Loop (HIL) and Software-in-the-Loop (SIL) testing methodologies and platforms (e.g., dSPACE, National Instruments)
- Desirable) Knowledge of advanced control techniques (e.g., adaptive control, optimal control, robust control) and estimation algorithms (e.g., Kalman filters, particle filters)
- Desirable) Experience with machine learning techniques for model identification, surrogate modeling, or data-driven predictions
- Desirable) Exposure to systems engineering processes, requirements management, and configuration control
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