Manager, Software - Perception; R3770

Founded in 2015, Shield AI is a venture‑backed defense‑tech company with the mission of protecting service members and civilians with intelligent systems. Shield’s products include Hivemind autonomy software and V‑BAT and X‑BAT aircraft, and the company’s technology actively supports operations worldwide.

This position is ideal for an individual who thrives on building advanced perception systems that enable autonomous aircraft to operate effectively in complex and contested environments. A successful candidate will be skilled in developing real‑time object detection, sensor fusion, and state estimation algorithms using data from diverse mission sensors such as EO/IR cameras, radars, and IMUs. The role requires strong algorithmic thinking, deep familiarity with airborne sensing systems, and the ability to deliver performant software in simulation and real‑world conditions.

We are seeking a skilled and motivated manager to lead technical teams and support direct projects integrating perception solutions for defense platforms.

Shield AI is committed to developing cutting‑edge autonomy for unmanned aircraft operating across all Department of Defense (DoD) domains, including air, sea, and land.

• Multidisciplinary Team Leadership – Lead teams across autonomy, integration, and testing by aligning technical efforts, resolving cross‑functional challenges, and driving mission‑focused execution. Balance hands‑on technical oversight with performance optimization, innovation, and clear stakeholder communication.
• Develop advanced perception algorithms – Design and implement robust algorithms for object detection, classification, and multi‑target tracking across diverse sensor modalities.
• Implement sensor fusion frameworks – Integrate data from vision systems, radars, and other mission sensors using probabilistic and deterministic fusion techniques to generate accurate situational awareness.
• Develop state estimation capabilities – Design and refine algorithms for localization and pose estimation using IMU, GPS, vision, and other onboard sensing inputs to enable stable and accurate navigation.
• Analyze and utilize sensor ICDs – Interpret interface control documents (ICDs) and technical specifications for aircraft‑mounted sensors to ensure correct data handling, interpretation, and synchronization.
• Optimize perception performance – Tune and evaluate perception pipelines for performance, robustness, and real‑time efficiency in both simulation and real‑world environments.
• Support autonomy integration – Work closely with autonomy, systems, and integration teams to interface perception outputs with planning, behaviors, and decision‑making modules.
• Validate in simulated and operational settings – Leverage synthetic data, simulation environments, and field testing to validate algorithm accuracy and mission readiness.
• Collaborate with hardware and sensor teams – Ensure seamless integration of perception algorithms with onboard compute platforms and diverse sensor payloads.
• Drive innovation in airborne sensing – Contribute novel ideas and state‑of‑the‑art techniques to advance real‑time perception capabilities for unmanned aircraft operating in complex, GPS‑denied, or contested environments.
• Travel Requirement – Members of this team typically travel around 10‑15% of the year to different office locations, customer sites, and flight integration events.

• BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience.
• Typically requires a minimum of 7 years of related experience with a Bachelor’s degree; or 5 years and a Master’s degree; or 4 years with a PhD; or equivalent work experience.
• 5+ years of experience in Unmanned Systems programs in the DoD or applied R&D.
• 2+ years of people leadership experience.
• Background in implementing algorithms such as Kalman Filters, multi‑target tracking, or deep learning‑based detection models.
• Familiarity with fusing data from radar, EO/IR cameras, or other sensors using probabilistic or rule‑based approaches.
• Familiarity with SLAM, visual‑inertial odometry, or sensor‑fused…