Staff Engineer, Aircraft Trajectory Modeling (R5787)

AI Chopping Block, Inc.; San Diego, Northern (CA, KY); Hybrid; USD 150,000 - 210,000; Full time

Job summary

Shield AI is seeking a Staff Engineer to design, develop, and integrate a real-time Run-Time Assurance product for autonomous aircraft. You will lead trajectory prediction and aircraft-response modeling, bridging safety logic with flight-control APIs, and collaborate with C/C++ engineers to deliver reliable software capable of flying, including support for Shield AI’s X-BAT UAV.

You will evolve TPAs, validate them with Python, simulation, HIL, and flight-test data, and guide multi-team efforts

Qualifications

• Minimum 7 years of related experience with a Bachelor's degree; or 6 years with a Master’s; or 4 years with a PhD.
• Expert Python for algorithms, numerical analysis, data processing, plotting, tuning workflows, and test automation.
• Proficient in C/C++, able to read production code, debug algorithm behavior, and write tests.
• Experience interfacing guidance, trajectory, or safety-critical algorithms with autopilots and validating through simulation, HIL, or flight-test data.

Responsibilities

• Lead development, tuning, and validation of trajectory-prediction and recovery-behavior models using aircraft data and constraints.
• Build Python-based analysis, simulation, HIL, and flight-test workflows to compare predicted vs observed behavior and maintain regression datasets.
• Integrate trajectory-prediction with autopilot interfaces, including vehicle-state inputs, latency, modes, and telemetry analysis.
• Support hardware integration and flight-test campaigns; contribute C/C++ implementation and debugging as needed.

Skills

Trajectory prediction

Python programming

C/C++ programming

Aerospace simulation

Autonomy / GNC

Education

Bachelor’s degree

Master’s degree

PhD

Tools

HIL

Flight-test hardware

Autopilot interfaces

Job description

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.

Job Description:

Shield AI is seeking aStaff Engineer to helpdesign, develop,andintegratea safety critical Run-Time Assurance(RTA)product.The Hivemind Foundations teamis responsible fora suite ofcoreproducts and capabilitiesthat brings resilient autonomy and intelligence toaircraftand other platformsoperatingin complex environments.The RTA product that theFoundationsteam developsis areal-time safety capabilitythat helpskeep 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.

What you'll do:

• 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.

Required qualifications:

• 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.

Preferred qualifications:

• 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.