Analytics Engineer, AV Safety Engineering

About the role

Work arrangement : Remote: This role is based remotely but if you live within a 50-mile radius of [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times per week, at minimum. 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, SAFE-ADS 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 Team The AV Safety Engineering Analytics team supports safety-related decision-making across GM by developing analytics, metrics, and evidence from vehicle, simulation, and external data sources. The team supports both proactive safety monitoring and targeted investigations, and works across stakeholder groups to support engineering, validation, verification, and program decisions by turning complex technical data into usable guidance. The AV Safety Engineering Analytics Engineer is an engineering role with a strong safety data science applied to physical systems focus, centered on developing the analyses, metrics, and evidence used to evaluate automated driving system safety and support decision-making. In this role, you will combine engineering judgment, data analysis, and statistical thinking to transform raw vehicle, simulation, and external data into safety metrics, investigations, and stakeholder-facing insights. You will work with cross-functional partners to define and productionize safety-relevant metrics, establish evidence and sufficiency criteria used to assess system performance and launch readiness, and communicate findings clearly to stakeholders. This role regularly supports systems, safety, testing, and verification activities by helping translate data into decision-useful metrics and evidence. Interactive visualizations and scalable data pipelines are important enablers in this role, helping analyses scale, increasing transparency, and turning complex results into usable stories for decision-making.

Responsibilities

• Define, prototype, and productionize safety and performance metrics for automated driving systems.
• Establish analytic approaches and sufficiency criteria that support safety assessment, development decisions, and launch readiness.
• Support proactive safety monitoring and targeted investigations tied to specific system-performance or safety questions.
• Support systems, safety, testing, and verification stakeholders by comparing real-world and simulation-based results, identifying gaps, and helping improve the representativeness of evaluation methods.
• Apply engineering and physics-based methods to process raw signals and derive meaningful representations of vehicle motion, driving context, and system behavior.
• Distinguish sensor or pipeline errors from meaningful real-world outliers using engineering judgment and data validation methods.
• Create interactive visualizations and reporting artifacts that communicate safety insights clearly, enhance transparency, and reduce barriers to interrogating source data in support of technical decision-making.
• Build and maintain analytics infrastructure that supports safety assurance across development, validation, and deployment.
• Develop reliable pipelines that ingest, transform, analyze, and publish data from vehicle systems, internal databases, simulation outputs, and external sources.
• Optimize analytics code and workflows for scalable, automated cloud execution.
• Your Skills & Abilities (Required Qualifications)
• Bachelor's degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field, or equivalent practical experience
• 5+ years of experience analyzing large-scale driving, vehicle, robotics, or similar engineering data
• 5+ years of experience in ADAS, autonomous vehicles, robotics, or a related technical domain
• Experience with statistics relevant to large-scale engineering data analysis, including sampling, bias management, and experimental design
• Experience transforming noisy time-series or sensor data into analysis-ready features or metrics
• Strong problem-solving skills and a proactive, learning-oriented mindset
• Strong communication and collaboration skills, with the ability to work effectively across technical teams
• Strong programming skills in Python and SQL
• Experience building and operating cloud-based analytics or data-processing workflows at scale
• Experience in some combination of the following is expected:
• Programming & Frameworks : Python, SQL
• Cloud & Big Data : cloud-based large-scale processing i

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Company Intel

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