Senior GNC Engineer, Modeling and Simulation

About Mach Industries

Founded in 2023, Mach Industries is a rapidly growing defense technology company focused on developing next-generation autonomous defense platforms. At the core of our mission is the commitment to delivering scalable, decentralized defense systems that enhance the strategic capabilities of the United States and its allies. With a workforce of approximately 350 employees, we operate with startup agility and ambition.

Our vision is to redefine the future of warfare through cutting-edge manufacturing, innovation at speed, and unwavering focus on national security. We are dedicated to solving the next generation of warfare with lethal systems that deter kinetic conflict and protect global security.

The Role

Mach Industries is seeking a Senior GNC Engineer - Modeling and Simulation to build, validate, and maintain the vehicle models and simulation environments behind GNC development for autonomous aircraft and related defense platforms. You will own significant modeling and simulation workstreams spanning flight dynamics, subsystem models, Monte Carlo analysis, and software- and hardware-in-the-loop testing, with a strong focus on comparing simulation results with flight-test data and turning that data into insights that improve our models and vehicles.

This is a hands-on individual-contributor role with meaningful technical ownership. You will turn equations of motion and engineering data into credible, efficient simulation tools, decide how much fidelity each question requires, and use flight-test data, statistical methods, and machine learning techniques to build the evidence the team needs to trust simulation results in design, verification, and flight-readiness decisions.

The ideal candidate combines strong flight-dynamics and physics-based modeling fundamentals with solid software engineering, data analysis, and practical test experience. You can work independently on complex problems, make sound engineering trade-offs, communicate model limitations and uncertainty clearly, and collaborate effectively with senior technical leaders and partner disciplines.

Key Responsibilities

Simulation Design and Technical Ownership

• Own the development and maturation of assigned simulation capabilities across requirements, architecture, model development, implementation, validation, and use in design and test decisions.
• Translate GNC, vehicle, and mission questions into simulation requirements, fidelity levels, model interfaces, output metrics, and validation criteria.
• Choose the right level of fidelity for each question, from point-mass and three-degree-of-freedom models through high-fidelity six-degree-of-freedom and multi-body simulations, balancing accuracy, run time, maintainability, and schedule.
• Implement equations of motion, reference frames, attitude representations, numerical integration, and event handling for real-time and faster-than-real-time simulation in C/C++ or Rust, along with MATLAB/Simulink.
• Define simulation architecture, model interfaces, data formats, and configuration approaches that allow models to be reused across vehicles and programs.
• Document assumptions, data sources, validity ranges, limitations, and validation evidence so that models and results are clear, reviewable, and reproducible.

Vehicle, Subsystem, and Environment Modeling

• Integrate aerodynamic databases derived from CFD, wind-tunnel testing, and flight data, including blending across flight regimes, damping and dynamic terms, control effectiveness, and behavior at table limits.
• Develop propulsion models for turbine engines, solid rocket motors, or other propulsion systems, covering thrust, fuel flow, throttle and derate behavior, thrust misalignment, jet damping, and gyroscopic effects.
• Work with Mechanical Engineering to maintain mass-property models, including center-of-gravity and inertia variation with fuel burn, expended components, and configuration changes.
• Model actuators and effectors, including bandwidth, rate and position limits, deadband, backlash, hysteresis, latency, and failure modes.
• Model IMUs, GNSS receivers, air-data sensors, seekers, and other sensors with representative noise, bias, scale-factor error, misalignment, latency, quantization, and failure behavior.
• Model atmosphere, winds, turbulence, and gusts, and incorporate weather data where mission analysis requires it.
• Model structural modes, launch and recovery systems, separation and deployment events, and other multi-body or constrained dynamics where they affect GNC performance.

Monte Carlo Analysis and Simulation Tools

• Plan and execute Monte Carlo analyses with defined dispersions on aerodynamics, mass properties, propulsion, sensors, actuators, winds, and initial conditions.
• Maintain uncertainty models and dispersion databases with documented sources, distributions, bounds, and rationale.
• Maintain data pipelines that bring aerodynamic, mass-property, propulsion, and test data into the simulation, with validation checks at each step.
• Build and improve tools for batch execution, parameter sweeps, sensitivity studies, statistical post-processing, and visualization of large simulation datasets.
• Make results deterministic, reproducible, and easy to find, compare, and share by managing random seeds, configuration files, model versions, input data, and software versions.
• Establish automated regression tests and clear pass/fail criteria for models, simulation scenarios, and GNC software changes as part of continuous integration.
• Profile and improve simulation performance so that large studies run efficiently on workstations and shared or cloud compute resources.

Validation and Test Support

• Lead the comparison of simulation predictions with flight-test data, reconstructing flights in simulation with the as-flown vehicle configuration, software, mass properties, winds, and commands.
• Quantify model error across the flight envelope, identify the sources of discrepancies, and drive evidence-based updates to models, uncertainty bounds, and dispersions.
• Turn flight-test data into actionable insights about vehicle performance, aerodynamics, propulsion, sensors, actuators, and GNC behavior, and communicate findings that shape design and test decisions.
• Apply machine learning and statistical techniques, such as regression, clustering, anomaly detection, and data-driven or surrogate modeling, to uncover trends and unexpected behavior across flight-test and simulation datasets.
• Build automated workflows that ingest flight logs, align them with simulation results, compute correlation metrics, and report findings quickly after each flight.
• Perform system identification and parameter estimation to build and update plant, actuator, propulsion, and sensor models from flight and ground-test data.
• Establish verification and validation approaches for simulation models, including unit tests, analytical checks, code-to-code comparisons, and comparison with component and ground-test data.
• Develop and support software-in-the-loop and hardware-in-the-loop environments that run flight software against the vehicle simulation with representative timing, interfaces, and fault injection.
• Support flight-test planning with pre-flight predictions, envelope and test-point analysis, and simulated rehearsals of nominal and off-nominal scenarios.