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:

We’re looking for a Staff Engineer to lead V-BAT’s fleet data analysis efforts. The V-BAT system produces rich data from real-world flight tests, fielded aircraft, production activity, and simulation runs.  

You will own the analysis pipelines, tooling, standards, and metrics that help the team understand fleet-wide system performance derived from information from software and hardware systems. You will facilitate deep studies into flight-critical sensor performance, navigation, communications, and other key aircraft behaviors. You will work closely with DevOps, Flight Software, Flight Test, Production, Quality, and Operations teams to turn complex aircraft data into actionable engineering insight. 

What you'll do:

• Lead the technical strategy for V-BAT fleet data analysis across fielded aircraft, flight test, simulation, production, and quality workflows 
• Own and improve the pipelines that transform raw flight, simulation, and fleet data into reliable engineering metrics, reports, and analysis products 
• Conduct fleet-wide studies to identify trends in hardware quality, system performance, reliability, and operational behavior 
• Analyze flight-critical sensor performance, GNSS-denied navigation performance, communications behavior, and other aircraft subsystems that are critical to mission success 
• Standardize the team’s analysis methods across online tools, Jupyter notebooks, automated Python scripts, and legacy Matlab workflows 
• Define best practices for analysis methods, data review, documentation, validation, reproducibility, and contribution workflows 
• Build automated Python workflows that make high-value metrics easily accessible to engineering, production, quality, and leadership teams 
• Partner with DevOps to build, deploy, maintain, and scale the infrastructure required for automated analysis pipelines and dashboards