Staff Engineer, Modeling & Simulation

About the Role

The Aircraft Simulation team is responsible for converting frontier autonomy into mission-ready aircraft. This role is centered on the commit-to-flight pipeline, encompassing deterministic aircraft and mission simulation, hardware-in-the-loop (HITL) and software-in-the-loop (SITL) integration, CI/CD, and automated flight qualification tooling. The core mission is to ensure AI flight operates safely, reliably, and at speed. As a Staff Modeling & Simulation Engineer, you will be dedicated to Shield AI's next-generation aircraft program. You will design, build, and scale novel aircraft subsystem models, develop infrastructure for robust automated testing for the X-BAT product line, and conduct system performance analysis. Your work directly enables evaluation of next-generation vehicles in realistic virtual environments. You will own the architecture of high-fidelity simulation models that underpin critical design decisions and validate vehicle behavior before physical flight. You will establish the frameworks and standards that allow the organization to iterate rapidly while maintaining rigorous safety and performance criteria. You will act as the primary technical authority for simulation fidelity, guiding trade studies and ensuring that virtual tests accurately represent the complexities of the real world. Ultimately, your contributions will determine the speed and confidence with which new autonomy capabilities are transitioned from simulation to flight.

Key Facts

What you'll do

• Architect and implement modular aircraft dynamics models that integrate into CI pipelines for automated validation of X-BAT behavior across diverse mission profiles.
• Construct deterministic environment scenarios that rigorously exercise autonomy stacks during HITL evaluations to uncover edge cases and regressions.
• Establish data pipelines that capture flight test and simulation results, enabling comparative performance reviews and data-driven model calibration.
• Guide model fidelity tradeoffs to ensure virtual campaigns accurately reflect real-world aerodynamic and sensor limits without unnecessary computational overhead.
• Support rapid iteration by automating test generation for Aechelon digital twin scenarios used across the Shield AI product line.
• Collaborate with control engineers to align subsystem models with flight controller expectations for V-BAT and X-BAT, ensuring cohesive system behavior.
• Drive traceability from high-level requirements to simulation artifacts, ensuring every test links to a clear operational need and design intent.
• Analyze system-level performance using telemetry and simulation data to identify root causes of anomalies and inform design improvements.
• Partner with perception and autonomy teams to integrate realistic sensor models and environmental interactions into the simulation pipeline.
• Champion best practices in modeling and simulation, mentoring engineers on techniques that improve reliability, reproducibility, and scalability.

Requirements

Hands-on experience constructing physics-based models for aircraft components and motion is mandatory for success in this role. You must write deterministic, scalable code capable of executing within automated test suites for flight qualification without introducing timing variability. Deep knowledge of aerospace principles, sensors, and navigation is essential to accurately represent the physics and constraints of flight. Proven ability to analyze system-level performance using telemetry and simulation data is required to drive insights from complex datasets. Experience with HITL or SITL test frameworks is mandatory for integrating simulation into CI workflows and enabling continuous validation. You must demonstrate the ability to collaborate closely with control, perception, and autonomy teams on next-generation aircraft programs to ensure alignment across disciplines. A strong commitment to rigorous methods and documentation is necessary to maintain traceability and support auditability in safety-critical contexts. The capacity to operate independently on complex technical problems while coordinating with multiple specialized teams is essential.

Nice to have

Experience with synthetic reality tools that support mission rehearsal and training scenarios is valued for enhancing the realism and utility of simulation environments.

Skills & Tools

Proficiency in programming languages such as Python or C++ for simulation and analysis is required to build efficient and maintainable models. Version control using Git-based workflows is necessary for collaborative model development and change tracking. Familiarity with middleware such as ROS or similar systems for real-time data distribution is expected to enable integration with existing infrastructure. Experience with simulation platforms including Unreal or custom tools for representing complex environments is required to create visually and physically accurate scenarios.

Practical notes

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