General Motors hiring AV Safety Engineering Analytics Simulation Engineer (GPSSC) in United States

The AV Safety Engineering Analytics team is seeking an Engineer with capabilities at the intersection of vehicle engineering, data science, cloud processing and scaled vehicle simulation. This team will maintain proficiency integrating continuously flowing data from vehicle systems, company databases, third-party services, federal agencies and state DOTs to inform system design and quantify driving performance. The team focuses on continuous up-time proactive analyses as well as supporting specific investigations. If you're passionate about the benefits of autonomous vehicle technology, committed to advancing safety through innovation, and love channeling big data into clear guidance, these roles offer exciting opportunities to make a meaningful impact on the future of transportation safety in a dynamic and fun environment. As part of the AV Safety Engineering Analytics team, you will work closely with cross-functional partners and internal customers to prototype, define, and productionize performance metrics that run in both the simulation environment and real-world. You will contribute to the Verification and Validation of automated driving systems. You will engage deeply with stakeholders to understand their challenges and needs, collaborate to develop solutions. What You’ll Do: Contribute to the development of data analytics infrastructure that supports safety assurance analytics, bridging simulation and on-road, addressing internal and external stakeholder needs across the phases of automated vehicle development and deployment, including both real-world and simulation data. Add and configure custom observers to evaluate simulation pass/fail criteria. Apply your engineering background and simulation experience to developing trustworthy and explainable methods for validating the safety performance of automated driving systems. Pilot and develop metrics for application in simulation and on-road for monitoring of development operations and deployment, and to establish sufficiency criteria for launch readiness. Collaborate with systems, safety, testing, and autonomy engineering teams to ensure simulation coverage of common and rare driving scenarios and ODD elements. Review simulation results to identify gaps between simulated and real-world performance. Develop methods for leveraging a variety of internal and external data sources for safety monitoring and contribute to the development of a reliable supply chain of continuously flowing data from a variety of sources (internal, external, simulation-based, on-road) to support safety assurance related activities. Drive the integration of simulation technologies into solutions or workflows, ensuring end user requirements are met. Implement cloud-based continuous up-time analytics solutions for monitoring driving simulated and real-world performance for safety and generating browser based interactive visualizations and periodic reporting artifacts. Actively contribute to ensuring the performance testing and validation performed in simulation is representative of real-world performance, and leads efficiently to deployment of trustworthy automated driving systems. Identify and drive opportunities to improve the efficiency, quality and transparency of safety analytics within GPSSC and across GM.

Bachelor’s degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field; or equivalent practical experience. 5+ years of experience working in a field that employs simulation for vehicle development and validation. 5+ years in autonomous vehicles, robotics or related field. Experience in the following: Simulation: Demonstrated experience with autonomous vehicle simulation platforms, scenario development. Programming & Frameworks: Python, SQL. Cloud & Big Data: 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. Statistics: Working familiarity with descriptive statistics, managing bias in large data mining activities, experimental design, sampling strategies. Dev Ops and Infrastructure as Code: CI/CD, versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform. Data Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy. Excellent communication and collaboration skills, with the ability to work effectively in a team environment. Strong problem-solving mindset and a proactive attitude towards learning and self-improvement.

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