Staff Engineer, Aircraft Trajectory Modeling (R5787)

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. 

Shield AI is seeking a Staff Engineer to help design, develop, and integrate a safety critical Run-Time Assurance (RTA) product. The Hivemind Foundations team is responsible for a suite of core products and capabilities that brings resilient autonomy and intelligence to aircraft and other platforms operating in complex environments. The RTA product that the Foundations team develops is a real-time safety capability that helps keep autonomy safe, predictable, and effective at the edge. 

As a senior member of the Hivemind Foundations team, you will lead applied trajectory prediction and aircraft-response modeling for the RTA. You will work at the boundary between safety logic, vehicle state, and autopilot or flight-control APIs, turning aircraft performance data, command-response assumptions, and test evidence into predictive models that guide safe recovery behavior. You will evolve Trajectory Prediction Algorithm (TPA) models, validate them with Python, simulation, HIL, and flight-test data, and partner with C/C++ engineers to deliver reliable software that can fly, including support for X-BAT, Shield AI's flagship Group 5 Collaborative Combat Aircraft (CCA) UAV. 

Lead development, tuning, and validation of TPA and recovery-behavior models using aircraft performance data, command-response assumptions, 3DOF/6DOF flyout concepts, wind effects, uncertainty bounds, and maneuver constraints. 

Build Python-based analysis, simulation, HIL, and flight-test workflows to compare predicted versus observed aircraft behavior, identify model gaps, tune parameters, and maintain regression datasets. 

Integrate and evaluate trajectory-prediction behavior through off-the-shelf or custom autopilot interfaces, including command modes, vehicle-state inputs, latency, mode transitions, control limits, and telemetry analysis. 

Support hardware integration and flight-test campaigns across relevant platforms; also help evolve trajectory-prediction and safety behaviors from single-aircraft use cases toward multi-agent collaborative CONOPS. Produce algorithm handoff artifacts and contribute scoped C/C++ implementation, testing, and debugging as needed. 

Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 6 years with a Master’s degree; or 4 years with a PhD; or equivalent work experience. 

Deep experience in trajectory prediction, aircraft-response modeling, aerospace simulation, robotics, applied autonomy, or GNC-adjacent domains, including 3DOF and/or 6DOF aircraft modeling concepts. 

Expert-level Python skills for algorithm development, numerical analysis, data processing, plotting, tuning workflows, and test automation. 

Working proficiency in C or C++, with the ability to read production code, debug algorithm behavior, write tests, make scoped implementation changes, and guide software engineers through algorithm intent. 

Demonstrated experience interfacing guidance, trajectory, or safety-critical algorithms with off-the-shelf or custom autopilots and validating behavior through simulation, HIL, flight hardware, or flight-test data. 

Ability to document model assumptions, handoff artifacts, and validation evidence while leading technical coordination across algorithms, software, systems, test, and platform teams. 

Experience tuning trajectory prediction, flyout, or vehicle-response models from simulation, HIL, or flight-test telemetry. 

Experience implementing or porting algorithms from Python, MATLAB/Simulink, or prototype models into C or C++ production software. 

Experience with Monte Carlo testing, scenario-based regression, validation metrics, envelope expansion, test-card planning, or flight-test safety reviews. 

Familiarity with high-reliability or safety-critical development practices, such as static analysis, coding standards, traceability, requirements-based testing, and verification evidence. 

Experience with CMake, Conan, Linux, CI/CD, embedded software workflows, or production software integration.