AV Safety Engineering Analytics Lead Engineer

Role Overview

• Provide the strategic vision and manage the design and development of infrastructure that supports safety assurance analytics addressing internal and external stakeholder needs across the phases of automated driving system development and deployment, including both real-world and simulation data.
• Oversee the design and integration of contemporary driving safety analytics relevant areas of expertise including AV engineering, AI/ML, large scale processing (cloud), and data science.
• Set team objectives, goals and metrics for tracking execution of these goals and delivery of enterprise KPIs.
• Oversee the piloting and definition of metrics for the monitoring of development operations and deployment, and establish sufficiency criteria for launch readiness.
• Oversee the identification of relevant data for supporting safety monitoring and the development of a reliable supply chain of continuously flowing data from a variety of sources (internal and external) to support safety assurance related activities.
• Oversee the development of cloud-based continuous up-time analytics pipelines that manage data from a raw form, through analyses, and into browser based interactive visualizations and periodic reporting artifacts.
• Oversee the selection of appropriate engineering
• and physics-based signal processing, sampling, filtering, smoothing, etc. to prepare raw signals for analyses and/or storage in a down sampled form.
• Oversee the selection of appropriate engineering-, physics-, and driving context-based inputs for the evaluation of AI/ML based analytics models.
• Actively engage with partners and seek input, provide technical expertise to inform leadership decision-making, and take ownership of technical projects.
• Define GM’s data sourcing and processing strategy for AV safety assurance needs, engage externally to influence evolving standards, and contribute to internal and external thought leadership that strengthens GM’s position in the autonomous vehicle ecosystem.
• Provide large scale data processing expertise across Global Product Safety, Systems, and Certification activities.
• Identify and drive opportunities to improve the efficiency, quality and transparency of safety analytics within GPSSC and across GM.
• Mentor and develop team members, fostering a culture of technical excellence and continuous learning.

Requirements

• Master’s degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field; or equivalent practical experience
• 10+ years of experience establishing and conducting large scale analyses of human and/or automated driving performance related data
• 5+ years in ADAS, autonomous vehicles, robotics or related field
• Demonstrated experience leading teams conducting large scale data analyses to support enterprise decision making
• Involvement in enterprise level strategy and roadmap development
• Experience using agile methods as well as longer range planning methods
• Experience recruiting and retaining talent, developing and motivating employees, conducting goal setting/alignment, conducting performance reviews.
• Proficiency in Python and SQL
• Extensive experience in cloud-based large scale process including notifications, queuing, serverless cloud functions, event driven processing, code as infrastructure, containerization, process monitoring, process optimization, identity and access management, service to service access, etc.
• Working familiarity with descriptive statistics, applied frequentist methods, applied Bayesian concepts, managing bias in large data mining activities, experimental design, sampling strategies.
• CI/CD, versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform
• Experience with Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy.

Tech Stack

• Cloud
• Docker
• Jenkins
• Kubernetes
• Numpy
• Pandas
• Python
• SQL
• Tableau
• Terraform

Benefits

• From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions.