AV Safety Engineering Analytics, AI/ML Engineer

Role Overview

• Contribute 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.

Requirements

• Master’s degree in Computer Science, Mechanical Engineering, Vehicle Engineering, Physics, or a related field; or equivalent practical experience focused on AI/ML
• 10+ years of experience in large scale analyses of vehicle related data
• 5+ years in safety-critical AI/ML systems in automotive engineering applications
• 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, Clustering
• 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.
• Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQL
• CI/CD, MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira, Jenkins, Poetry, Terraform
• Tableau, PowerBI, Plotly/Dash, Shiny, Pandas, NumPy
• Proven track record providing technical leadership in AI/ML applied to safety-critical systems
• 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.

Tech Stack

• Cloud
• Docker
• Java
• Jenkins
• Kubernetes
• Numpy
• Pandas
• PySpark
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tableau
• Tensorflow
• Terraform

Benefits

• Company Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate.