Safety Data Analysis Engineer – AV Operational Safety and Issue Response (GPSSC)
General Motors
- Location
- Remote - United States
- Work model
- Remote
- Level
- Mid
- H-1B history
- 267 approvals (FY2023)
- Posted
- Sep 3, 2026
About this role
Job Description
At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale. The Safety Assurance for Effective Autonomous Driving Software (SAFE-ADS) department is part of GM’s Global Product Safety, System, and Certification organization. Our mission is to help GM deliver trustworthy automated-driving products. As the central authority for automated-driving system safety, the department brings together experts from across the company to develop and maintain a comprehensive safety case, including safety performance indicators for GM’s automated-driving technologies. GM’s vision is zero crashes, zero emissions, and zero congestion, and autonomous vehicle safety is essential to achieving that vision.
The Role
The AV Safety Engineering Analytics team is seeking an experienced Safety Data Analysis Engineer to provide engineering support for AV Operation al Safety and Issue Response . This is a safety engineering role focused on data-driven issue response and investigation. You will combine engineering judgment, safety analysis, and strong data skills to investigate potential issues, organize event-related data, and develop evidence that supports AV Safety Assurance and AV Operatio nal Safety decisions. The role is designed for an engineer who is highly proficient in navigating, integrating, designing, and analyzing large-scale data. You will support rapid, highly iterative investigations involving specific vehicles, locations, software releases, event sequences, operational conditions, and other issue-relevant dimensions. The scale of AV operations, including potential issues, actual events, readiness drills, and ongoing software releases, creates a need for rapid, detailed, event-driven analyses. At the same time, you will help convert repeatable investigation patterns into reusable analytics and issue-detection methods.
What You'll Do
Respond collaboratively to AV Safety Strategy and Assurance and AV Operations requests for potential issue-specific analyses, event-related data organization, and root-cause investigations. Collaborate with Engineering, Operations, and Safety teams to investigate potential AV issues and conduct root-cause analyses. Translate investigation needs into clear analysis requirements, data requirements, analytical designs, and execution plans in collaboration with requesting teams. Establish and support rapid incident-response and targeted-analysis capabilities that leverage standing data pipelines as well as unique issue - and incident-relevant data sources. Integrate vehicle telemetry, time-series signals, event and incident records, readiness-drill data, software-release information, location and operational context, and other relevant sources into analysis-ready datasets. Distinguish data-quality , sensor, instrumentation, and pipeline issues from meaningful operational outliers using engineering judgment and data validation methods. Deliver concise, technically rigorous analysis findings, readouts, and evidence that help partner teams assess potential operations safety issues and determine appropriate next steps. Identify investigation workflows that can be generalized into reusable metrics, detection methods, pipelines, or analysis tooling without losing the flexibility required for issue investigation . Apply engineering and physics-based methods, including signal decomposition, filtering, smoothing, sampling, and feature construction, to transform