Engineer

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 Human Machine Teaming (HMT) Group at Shield AI is hiring an Engineer II or Senior Engineer to join our HMT Experimentation capability. At Shield AI, HMT refers to the design, management, and assurance of the adaptive relationships between operators and Hivemind-enabled mission autonomy—not just an interface problem, or a function-allocation exercise.

Working alongside the HMT Experimentation Lead, who owns HMT experimentation, research, and assessment, you will assist in establishing the HMT Experimentation Lab and help run the applied research and development (R&D) program behind our HMT design decisions. You will test whether collaborative work requirement profiles—the adaptive relationships—hold in practice, and conduct robustness testing that establishes where teaming works, degrades, and approaches failure.

If rigorous applied HMT-specific experimentation on in-development and fielded autonomous systems is what you want, this is your opportunity to make a lasting impact.

What you'll do:

• Design and run HMT experiments that instantiate baseline and context-sensitive collaborative work requirements profiles and test whether these hold under varied conditions.
• Conduct robustness and resilience testing, perturbing, for instance, communications, sensor data, autonomy behavior, workload, and time pressure to find the limits of teaming.
• Characterize the context sensitivity of requirements profiles across environments, platforms, and operator populations.
• Extend operator-in-the-loop (OITL) and live-virtual-constructive (LVC) testing with HMT-specific experimentation.
• Define HMT-specific quantitative and qualitative measures of performance, effectiveness, and success, build assessment batteries for operator situation awareness, workload, decision making, calibration, reliance, and trust, prepare research protocols, and analyze human-machine system data.