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 world and aeronautical models 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 aeronautical physics models (aerodynamics, propulsion, environmental, structural, etc.) for use in simulation and evaluation.
- 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 1+ years of aerospace modeling experience.
- C++ foundation with experience implementing numerical methods.
- Demonstrated experience with aerospace models such as: Aerodynamics (lift/drag curves, stability derivatives, dynamic response), Atmosphere/environment (COESA, ICAO, gravity, wind models), Vehicle dynamics.
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.
- Background in multi-body dynamics or flight mechanics
- Knowledge of uncertainty quantification and statistical analysis methods.
- 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.
#LI-SM1