Staff Software Engineer, Autonomy - Behavior and Motion Planning (R6007) at Shield AI in San Diego, CA

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. What you'll do: • Research, design, and implement state-of-the-art algorithms for autonomous trajectory planning and control for single and multi-agent systems • Build reliable, robust, and maintainable software, primarily in modern C++, with Python used for prototyping, analysis, automation, and testing. • Develop comprehensive tests and evaluate planning stack performance in simulation and, where applicable, on real hardware. • Diagnose complex algorithmic and software failures, including concurrency, performance, and integration issues. • Collaborate with teams across the organization to refine requirements and deliver capabilities that meet operational needs. • Contribute to the design and architecture of scalable, maintainable autonomy and software architecture • Develop analysis tools to allow for rapid iteration • Take new product features from ideation to verification, to flight testing, and all the way to maturation • Mentor and provide technical guidance to junior engineers • Stay current with the latest advancements in robotics, and apply them to solve challenging problems Projects you might work on: • Motion planning that produces safe, dynamically feasible behavior under real-world constraints. • Collaborative and distributed motion planning for multi-agent scenarios. • Incorporating various types of motion and dynamics models into the planning stack. • Write software that allows the customer to interact with payloads safely. • Planning approaches that incorporate vision-language models (VLMs), vision-language-action models (VLAs), or other learned components. • Validate the planning stack using software-, hardware-, and vehicle-in-the-loop simulation of complex autonomous missions. Required qualifications: • Master's degree in computer science, Robotics, or a related field and 5+ years of relevant professional experience or PhD with 4+ years of relevant experience. • Demonstrated experience through professional work, internships, research, or substantial projects building, testing, and debugging nontrivial C++ software for motion planning, robotics, autonomous systems, or a closely related algorithmic domain. • Experience writing and debugging modern C++, including safe use of common concurrency patterns and the ability to diagnose issues such as data races, deadlocks, and lifetime errors. • Working experience with Python for prototyping, analysis, automation, or testing. • Strong software engineering fundamentals, including algorithms, data structures, testing, debugging, version control, CI/CD, and maintainable software design. • Practical experience developing and debugging software on Linux. • Ability to communicate technical decisions and collaborate effectively across disciplines. Preferred qualifications: • Experience with motion planning for any platform (aerial, maritime, space, or ground), working on decentralized or distributed planning for multi-agent systems, or trajectory optimization. • Experience applying VLMs or VLAs to motion planning in safety critical systems. • Experience delivering software for fielded, real-time, safety-critical, or resource-constrained autonomous systems. • Experience with software-in-the-loop or hardware-in-the-loop simulation, performance profiling, or distributed systems. • Evidence of relevant technical depth through fielded systems, applied research, publications, open-source contributions, or robotics and autonomy competitions. Full-time regular employe