Engineer II, GNC, Ranger (R6386)

Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.

Develop, implement, and test Auto-Taxi and Automatic Take off and Landing algorithms and supporting flight-mode logic. Design and implement inner- and outer-loop flight control laws, guidance algorithms, and control allocation functions for conventional takeoff, wing-borne flight, and landing, with technical guidance and design reviews from senior engineers. Support integration between flight autonomy and mission autonomy through A-GRA-aligned interfaces, implementing and testing command handling, flight-mode transitions, vehicle-status reporting, and contingency responses within the program’s defined architecture. Develop and validate vehicle and subsystem models within an existing high-fidelity six-degree-of-freedom (6DOF) simulation environment, including aerodynamics, mass properties, propulsion, sensors, actuators, and ground-contact dynamics. Execute verification activities, including linear and nonlinear analysis, Monte Carlo campaigns, sensitivity studies, disturbance and degraded-operation testing, and software- and hardware-in-the-loop testing. Analyze simulation, ground-test, and flight-test results to identify discrepancies, investigate root causes, and verify updates to control algorithms and simulation models. Support flight-test preparation and execution through pre-flight performance predictions, test-point analysis, telemetry review, and post-flight reconstruction. Collaborate with propulsion, aerodynamics, actuation, state estimation, embedded software, and autonomy engineers to resolve integration issues. Document designs, assumptions, and test results, and participate in code and technical reviews.

2+ years of relevant experience in flight controls, GNC, aircraft simulation, or autonomous aircraft development. Directly relevant graduate research may be considered toward this experience. A strong foundation in aircraft flight dynamics, feedback control, 6DOF equations of motion, coordinate transformations, and numerical methods. Experience performing one or more of the following technical assignments: Vehicle modeling and simulation: Implementing or validating mathematical models of aircraft or subsystem dynamics within a 6DOF simulation. Control law design and allocation: Developing or evaluating fixed-wing control laws, actuator-command mixing, or control allocation functions. Guidance and trajectory validation: Implementing or testing trajectory validation algorithms, path-following, or flight-mode sequencing algorithms. Verification and test analysis: Performing simulation campaigns, sensitivity studies, test-data analysis, or comparisons between predicted and measured aircraft behavior. Exposure to C++ development environments and implementing and debugging engineering algorithms. Strong problem-solving and communication skills, with the ability to work through defined technical problems, communicate assumptions and limitations, and incorporate feedback from senior engineers.

Exposure to Auto-Taxi or ATOL algorithm development, including evaluation of approach and touchdown performance, crosswind response, runway tracking, go-around behavior, or degraded navigation conditions. Direct experience implementing or testing A-GRA-aligned interfaces or integrating a flight-autonomy system with a separate mission-autonomy provider. Strong C++ skills, including object-oriented development, unit testing, and implementation of algorithms subject to real-time execution constraints. Familiarity with aircraft navigation, air data, altitude sensing, and the effects of sensor errors, latency, and data validity on flight control and ATOL performance. Experience with software- or hardware-in-the-loop environments, Linux-based development, Git, and automated testing or CI/CD pipelines. Exposure to propulsion modeling, including integration of steady-state or transient NPSS models or model outputs into a 6DOF simulation environment. Familiarity with flight safety, airworthiness verification, and preparation of supporting artifacts such as simulation descriptions, verification reports, or flying-qualities analyses. Experience working through iterative development and test cycles, including investigating discrepancies and verifying corrective changes