JOB DESCRIPTION:
The Software Integration & Operations (SIO) group turns frontier autonomy into mission-ready aircraft. We own the commit-to-flight pipeline—deterministic aircraft and mission simulation, HIL/VIL integration, CI/CD, automated flight qualification testing, and release engineering. Our goal is simple: make AI fly—safely, repeatably, and fast.
As a Modeling & Simulation Engineer, you will be responsible for improving and adding to our sensor and communications model suite so that our operator training and internal engineering pipelines have a seamless translation from sim to real results.
What You'll Do
Develop and enhance radar sensor models for use in simulation and evaluation of aeronautical vehicles.
Translate theoretical models into efficient, reliable C++ implementations with a focus on numerical accuracy and performance.
Validate models against real-world data and authoritative references, including field test data and calibration procedures.
Collaborate with simulation and training application teams to ensure models integrate cleanly into operator-facing tools.
Design automated validation and regression testing strategies for mathematical models to ensure fidelity across releases.
Prototype and evaluate new modeling techniques (reduced-order models, uncertainty quantification, machine learning–based surrogates) to push the state of the art.
Document assumptions, equations, and validation results so that both engineers and operators can trust model outputs.
Required Qualifications
BS or higher in Aerospace Engineering, Applied Math, Physics, or related field with 5+ years of aerospace modeling experience.
C++ foundation with experience implementing numerical methods.
Demonstrated experience with aerospace models such as: Radar sensors, Radio communications systems
Experience validating simulations against real-world or experimental data.
Ability to write clear documentation explaining assumptions, limitations, and expected behaviors of models.
Preferred Qualifications
1+ years of experience working on pilot/operator training systems.
Experience with Eigen or SciPy for model prototyping and validation.
Familiarity with state estimation sensor models (GPS, IMU, Gyro, etc) for simulation environments.
Demonstrated experience with payload sensor models including: Laser senros, IR and optical cameras
Knowledge of uncertainty quantification and statistical analysis methods.
Experience with parallelization or GPU acceleration for compute-heavy models.
Strong problem-solving mindset with a collaborative and detail-oriented approach.
Familiarity with Python for test automation and data analysis pipelines.
Passion for aerospace and autonomous vehicle systems.
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