Research, design, and implement discrete optimization and mission-planning algorithms for multi-agent autonomous systems; improve and maintain C++ mission-planning software; define technical roadmaps; mentor engineers; perform full software and hardware-in-the-loop simulations.
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 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).
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Full-time regular employee offer package:
Pay within range listed + Bonus + Benefits + Equity
Temporary employee offer package:
Pay within range listed above + temporary benefits package (applicable after 60 days of employment)
Salary compensation is influenced by a wide array of factors including but not limited to skill set, level of experience, licenses and certifications, and specific work location. All offers are contingent on a cleared background and possible reference check. Military fellows and part-time employees are not eligible for benefits. Please speak to your talent acquisition representative for more information.
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Shield AI is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status. If you have a disability or special need that requires accommodation, please let us know.
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