Staff Software Engineer, Autonomy - Behavior and Motion Planning (R6007)

About the role

Shield AI is a venture-backed defense-tech company developing intelligent systems including Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation technologies. The Pilot team creates algorithms and production software enabling autonomous decision‑making in uncertain, dynamic environments. As a member of the Behavior and Motion planning team you will develop, deploy, and optimize path planning algorithms for autonomous systems operating in complex real‑world settings.

Responsibilities

• 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++, using Python 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 cross‑functional teams 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, flight testing, and 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 include motion planning that produces safe, dynamically feasible behavior under real‑world constraints; collaborative and distributed motion planning for multi‑agent scenarios; incorporating various motion and dynamics models into the planning stack; writing software for safe customer interaction with payloads; planning approaches that incorporate vision‑language models (VLMs) or vision‑language‑action models (VLAs); validating the planning stack using software‑, hardware‑, and vehicle‑in‑the‑loop simulation of complex autonomous missions.

Requirements

• 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 building, testing, and debugging nontrivial C++ software for motion planning, robotics, autonomous systems, or a closely related algorithmic domain (through professional work, internships, research, or substantial projects).
• Experience writing and debugging modern C++, including safe use of common concurrency patterns and ability to diagnose data races, deadlocks, and lifetime errors.
• Working experience with Python for prototyping, analysis, automation, or testing.
• Strong software engineering fundamentals: algorithms, data structures, testing, debugging, version control, CI/CD, 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.

Benefits

• Bonus
• Benefits (health, retirement, etc.)
• Equity
• Temporary benefits package available after 60 days for temporary employees

Pay

$200,000 - $300,000 a year

Schedule

Full‑time regular employee