Shield AI
Staff Software Engineer, Autonomy Capabilities (R5368)
San Mateo, California
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hirly's read of this role
- Role family
- Engineering
- Seniority
- Lead / management
- Stated salary
- $234,240 – $351,360 per year
- Country
- US
- Work mode
- On-site / unstated
- First seen by hirly
- 11 Sept 2026
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the posting
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 .
The Autonomy Capabilities Team develops functionality to automate single- and multi-agent teams of platforms in pursuit of mission objectives and acts as a central discipline-aligned focal-point for encouraging consistent usage of technical approaches across business portfolios. This team operates at all levels of the command-and-control hierarchy (from advanced control laws, through motion planning, and up to advanced tactical behaviors and multi-agent coordination) and across a multitude of mission sets, platform types, and operational domains (e.g., air, sea, space). The team puts a strong emphasis on both fundamentals (e.g., aircraft kinematics/dynamics, trajectory design, optimization, information fusion, efficient algorithms, etc.) and software integration skillsets (e.g. structures, protocols, threading, interface management, etc.) to bring market-differentiating capabilities to customers that seamlessly operate within their integration contexts.
This position is perfect for an individual who enjoys solving complex problems across a diverse set of programs and integration contexts. An ideal candidate is expected to address operational system needs through a multitude of advanced methodologies that blend traditional control system approaches with advanced optimization. Developed solutions are expected to be integrated into real-world platforms with near-term program impacts and rewards and so balancing theory with practice and rigorous implementation is paramount.
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