Senior Engineer, Shield AI

• This position is perfect for an individual who enjoys solving the most complex problems across a portfolio of diverse domains and modalities
• 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 Bachelor’s degree; or 4 years and a Master’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 robotics technologies related to motion planning, behavior modeling, decision-making, or autonomous system design
• Significant experience with unmanned system technologies and accompanying algorithms (specifically air domain)
• Experience with simulation tools and environments (e.g., AFSIM, NGTS) 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

Desirable

• An ideal candidate is expected to apply classical autonomous techniques, algorithms, and theory to various platforms in multiple tactical scenarios
• Tactical Autonomy Design – This role will design tactical autonomy algorithms to enable unmanned aircraft to perform complex missions across air, land, and sea domains with minimal human supervision
• High-Performance Software Development – Develop high-performance software modules that incorporate planning, decision-making, and behavior execution strategies for dynamic and adversarial environments
• Behavior Architecture Implementation – Implement and test behavior architectures that enable multi-agent coordination, target engagement, reconnaissance, and survivability in contested scenarios
• Hybrid Autonomy Integration – 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
• Cross-Functional Collaboration – Collaborate with cross-functional teams including perception, planning, simulation, hardware, and flight test to ensure seamless integration of autonomy solutions on real-world platforms
• Deployment & Field Testing – Deploy autonomy capabilities to real platforms and participate in field tests and flight demos, validating performance in operationally relevant conditions
• Mission Data Analysis – Analyze mission logs and performance data to diagnose failures, optimize behavior models, and inform iterative development
• R&D and Roadmapping – Contribute to the autonomy roadmap by researching and prototyping new algorithms, identifying tactical capability gaps, and proposing novel solutions that advance Shield AI’s mission
• Program Support & Adaptation – Support defense-focused programs and customer needs by adapting autonomy solutions to evolving mission sets, compliance requirements, and operational feedback
• Travel Requirement – Members of this team typically travel around 10-15% of the year (to different office locations, customer sites, and flight integration events)