Senior Software Engineer, Autonomy Capabilities (R5339)

Shield AI•San Mateo, CA

6d•$195,840 - $293,760

About The Position

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 AIâ€™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.

Requirements

BS/MS in Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, and/or similar degree, or equivalent practical experience
Typically requires a minimum of 5 years of related experience with a Bachelorâ€™s degree; or 4 years and a Masterâ€™s degree; or 2 years with a PhD; or equivalent work experience.
Proficiency in programming languages such as C++ and Python, and familiarity with real-time operating systems (RTOS)
Significant background in one or several robotic technology areas related to control systems, motion planning, optimization, tactical behaviors, and/or distributed decision-making
Significant experience with unmanned system technologies and accompanying algorithms (ideally in the space domain specifically)
Experience with simulation tools and environments (e.g., AFSIM, NGTS, or similar) for testing and validation
Strong problem-solving skills, with the ability to troubleshoot and optimize system performance
Excellent communication and teamwork skills, with the ability to work effectively in a collaborative, multidisciplinary environment
Ability to obtain a SECRET clearance or higher

Nice To Haves

Experience in multiple aerospace domains (e.g., air, land, sea, space)
Experience across a breadth of methodologies from classical control through optimization and applied ML/RL
Background in collaborative behaviors and swarm robotics
Familiarity with domain-relevant DoD or government programs
Hands-on experience supporting integration events, customer demos, and/or live exercises

Responsibilities

Design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision.
Develop high-performance software modules that incorporate planning, decision-making, and behavior execution strategies for dynamic and adversarial environments.
Implement and test behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios.
Work at the intersection of classical autonomy and machine learning, blending rule-based systems with learning-based methods such as reinforcement learning to achieve robust, adaptive behavior.
Collaborate with cross-functional teams including perception, planning, simulation, hardware, and flight test to ensure seamless integration of autonomy solutions on real-world platforms.
Deploy autonomy capabilities to real platforms and participate in field tests and flight demos, validating performance in operationally relevant conditions.
Analyze mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development.
Contribute to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AIâ€™s mission.
Support defense-focused programs and customer needs by adapting autonomy solutions to evolving mission sets, compliance requirements, and operational feedback.