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.
Job Description:
The Discrete Planning Group is a part of the Autonomy Team for Hivemind Enterprise Product. The group is an agile set of engineers focused on researching and developing state-of-the-art algorithms that drive intelligent and confidence-inspiring flight behaviors while accounting for an uncertain and dynamic world. As a member of the group, you will work at the intersection of artificial intelligence, discrete optimization, and motion planning. You will architect and write high-quality software for core systems, set standards for software engineering, refine technical requirements, drive strategic technical improvements, and mentor other engineers.
What you'll do:
Research, design, and implement state-of-the-art algorithms for optimal task allocation, scheduling and temporal sequencing of heterogeneous teams of autonomous vehicles (land, air, other).
Solidify and improve existing C++ based mission planning software applicable across disparate vehicle types and compute platforms.
Work with our engineers, program managers, and product managers to define a technical roadmap for future autonomy solutions and SDK offerings.
Projects you might work on:
Breakdown a mission into assignable tasks based on agent capabilities such as navigating in contested and denied environments and adapting to mission changes in real-time.
Construct feasible and optimal action plans for distributed teams of autonomous vehicles that also minimize human operator workload.
Full software and hardware-in-the-loop simulation of complex multi-agent missions.
Required qualifications:
Typically requires a PhD with graduate work in Optimization or Operations Research. Master’s degree with 4 or more years of work in the same areas.
Expert in Integer or Mixed Integer Linear Programming, Constraint Programming, Convex Optimization, Multi-Objective-Optimization and using established solvers such as Gurobi, Google-OR, CPLEX etc.
Significant experience in implementing algorithms in C++ and using debuggers such as gdb to troubleshoot execution within a multi-threaded environment.
Ways to stand out:
Past publications on Optimization(google scholar profile).
