Senior Engineer, Human Machine Teaming (R5649)

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-BATaircraft, 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 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. Translate findings into prioritized recommendations, revisions to requirements profiles, and feature requests for Development Leads, and co-create a shared HMT body of knowledge. Contribute evidence to the HMT maturity scale, risk assessment, and the Hivemind assurance case, and report results to engineering and customer audiences. Travel to company, test, demonstration, and customer locations to conduct project work (approximately 25%). Required qualifications: Engineer II: typically requires a minimum of 2 years of related experience with a Bachelor's degree; or 0 years and a Master's degree; or a PhD without experience. Senior Engineer: typically requires a minimum of 3–5 years of related experience with a Bachelor's degree; or 2–4 years and a Master's degree; or 2 years with a PhD; or equivalent work experience. A degree in human factors engineering or psychology, cognitive systems engineering, applied cognitive science, industrial and systems engineering, or a related field. Working knowledge of human machine teaming, human factors, and human performance theory and measurement, including cognitive task analysis and knowledge elicitation. Sound grasp of quasi-/experimental design, between- and within-participant designs, and control of extraneous confounds, experience conducting human participants research, proficiency with multivariate statistical analyses and tools (e.g., SPSS, R, Python), and ability to translate results into design recommendations. Experience in multidisciplinary settings, and ability to work in complex, ambiguous problem spaces and produce clear and structured artifacts. Strong teamwork and collaboration skills, and written and verbal communication skills. Preferred qualifications: Hands-on experience with human machine teaming, MUM-T, human-robot interaction, or applied artificial intelligence, specifically in mission autonomy applications such as UAV Groups 1–5. Experience with developmental or operational test and evaluation, including OITL and LVC testing. Experience in scenario design, and with modeling and simulation tools and methods. Familiarity with trust and reliance measurement and experience with robustness, resilience, or degraded and off-nominal scenario testing. Experience with mission planning, C2, and battle management concepts, processes, and doctrine, and with ground control stations or HMIs. Familiarity with traceability methods, or with AI assurance frameworks. Willingness to use agentic AI to speed the path from design concept to fielded product. Prior work with military operators, pilots, or subject matter experts, or an aircrew background. U.S. DoD Secret clearance eligible, with the ability to obtain and maintain Top Secret, SCI, and/or SAP-level access. U.S. citizenship required. #J-18808-Ljbffr AI Chopping Block