हम आपके अनुभव को बेहतर बनाने, साइट उपयोग का विश्लेषण करने और हमारे मार्केटिंग प्रयासों में सहायता के लिए कुकीज़ और समान तकनीकों का उपयोग करते हैं। गोपनीयता नीति
पोस्ट का समय:
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. 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. 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. 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. Past publications on Optimization(google scholar profile).