AV Safety Engineering Analytics AI/ML Engineer (GPSSC)

Job DescriptionWork 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 AV Safety Engineering Analytics  team is seeking and AI/ML Engineer with capabilities at the intersection of vehicle engineering, AI/ML and cloud processing. The AV Safety Engineering Analytics team is the resource supporting teams and stakeholders from around the company bring a broad range of data and analytics capabilities to bear in AV safety related decision making. 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 AI and ML, and love channeling big data into clear guidance, this role offers 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 be the subject matter expert in appropriately implementing AI/ML methods for safety assurance analytics. You will work closely with cross‑functional partners and internal customers to consider opportunities for appropriate use of AI/ML methods to organize safety related data, and evaluate driving performance.  You will engage deeply with other members of the AV Safety Engineering Analytics team, and stakeholders, to understand their challenges and needs, collaborate to develop solutions using your expertise in AI/ML. What You’ll DoContribute to the development of data analytics infrastructure that supports safety assurance analytics addressing internal and external stakeholder needs across the phases of automated vehicle development and deployment, including both real-world and simulation data.Apply your AI/ML expertise to developing trustworthy and explainable methods for validating the safety performance of an AI/ML based automated driving system.Mentor and develop team members in the appropriate implementation of AI/ML to support the mission of the SAFE-ADS department and AV Safety Engineering Analytics team.Pilot and develop metrics for monitoring of development operations and deployment, and establish sufficiency criteria for launch readiness.Develop methods for leveraging a variety of internal and external data sources for AI/ML based safety monitoring and contribute to 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.Implement cloud-based continuous up-time analytics solutions for monitoring driving performance for safety and generating browser based interactive visualizations and periodic reporting artifacts.Actively contribute to determining appropriate use of AI/ML approaches within automated driving systems and provide technical expertise to inform leadership decision-making regarding mechanisms for ensuring they are trustworthy and explainable.Through AI/ML expertise, contribute to the definition of 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.Represent SAFE-ADS in AI/ML related discussions 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.Your Skills & Abilities (Required Qualifications)Master’s degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field; or equivalent practical experience focused on AI/ML10+ years of experience in large scale analyses of vehicle related data5+ years in safety-critical AI/ML systems in automotive engineering applicationsExperience in the following:Machine Learning & AI:  Extensive experience in building large-scale models with significant focus on E2E validation. Experience using Large Language Models (LLMs), Generative AI, RAG, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, ClusteringAI Standards and Evolving Regulations: Understanding of ISO/PAS 8800, NIST AI Risk Management Framework, EU AI Act (2024-2027), other applicable industry standards and best practices for autonomous vehicles, aerospace and/or robotics.Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQLCloud & Big Data: 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. (Microsoft Azure - Data Lake, Machine Learning, Databricks), (AWS - S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform)Deployment & MLOps: CI/CD, MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, TerraformData Analysis & Visualization: Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPyProven track record providing technical leadership in AI/ML applied to safety-critical systemsExcellent communication and collaboration skills, with the ability to work effectively in a team environmentStrong problem-solving mindset and a proactive attitude towards learning and self-improvementWhat Will Give You A Competitive Edge (Preferred qualifications)Record of involvement in public AI/ML relate