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
Work with Software, GNC, Embedded, Flight Test, Systems, Production, Quality, and Operations teams to define metrics that reflect aircraft performance and product health
Support anomaly investigations, root-cause analysis, release readiness, production quality improvements, and customer-impacting fleet investigations with rigorous data analysis
Mentor engineers on effective data analysis practices and raise the quality bar for data-driven engineering decisions across the V-BAT team
Required qualifications:
5+ years of relevant experience with a Bachelor’s degree in Computer Science, Data Science, or a related technical field
Strong Python skills and experience building data analysis tools, automated analysis workflows, or data pipelines
Experience analyzing data from fielded physical products such as aerospace systems, automotive systems, robotics platforms, commercial electronics, IoT devices, industrial equipment, or similarly complex real-world systems
Experience working with large, messy datasets from deployed products, test events, simulations, production systems, or operational environments
Ability to translate ambiguous engineering questions into structured analysis, sound conclusions, and clear recommendations
Strong communication skills and the ability to influence cross-functional engineering, production, quality, and operations teams
Preferred qualifications:
Experience with flight test data, aircraft telemetry, UAVs, robotics, autonomy, embedded systems, GNC, navigation systems, communications systems, or aerospace sensor suites
Experience with Matlab, Jupyter, or similar analysis and visualization tools
Experience building production-quality dashboards, automated reports, data products, or fleet-health monitoring systems
Experience with databases, cloud storage, data lake architectures, time-series databases, telemetry systems, or data cataloging
Experience with anomaly detection, trend analysis, statistical process control, reliability analysis, regression detection, or automated validation of complex systems
