The Simulation Modeling Engineer enhances aerospace physics models for simulations, validates models against real data, and collaborates on training tools.
JOB DESCRIPTION:
Shield AI is building autonomous aircraft that push the limits of aviation. The Software Integration & Operations (SIO) team builds and sustains the release systems that make safe, rapid, and repeatable deployment of aircraft software possible.
As a Simulation Modeling Engineer, you will be responsible for improving and adding to our world and aerospace models so that our operator training and internal engineering pipelines have a seamless translation from sim to real results. The capabilities you deliver will directly impact our ability to deliver new features to operators, with speed & ease.
WHAT YOU'LL DO:
Develop and enhance aerospace physics models (aerodynamics, propulsion, environmental, sensor, etc.) for use in simulation and evaluation.
Translate theoretical models into efficient, reliable C++ and Matlab implementations with a focus on numerical accuracy and performance.
Validate models against real-world data and authoritative references (e.g., COESA atmosphere, wind tunnel data, flight test logs).
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 4+ years of aerospace modeling experience.
1+ years of experience working on pilot/operator training systems.
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
Experience validating simulations against real-world or experimental data.
Ability to write clear documentation explaining assumptions, limitations, and expected behaviors of models.
Passion for aerospace and autonomous vehicle systems.
Strong problem-solving mindset with a collaborative and detail-oriented approach.
PREFERRED QUALIFICATIONS:
Experience with Eigen, MATLAB/Simulink, or SciPy for model prototyping and validation.
Background in multi-body dynamics or flight mechanics.
Familiarity with sensor models (GPS, IMU, radar, etc.) for simulation environments.
Knowledge of uncertainty quantification and statistical analysis methods.
Experience with parallelization or GPU acceleration for compute-heavy models.
Familiarity with Python for test automation and data analysis pipelines.
Experience contributing to simulation pipelines in aerospace, defense, or robotics domains.
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