Principal AI Safety Engineer

Responsibilities: - Lead the development of AI safety strategies for ADS and establish safety engineering guidance and sufficiency criteria. - Actively engage with partners and seek input, provide technical expertise to inform leadership decision-making, and take ownership of technical projects. - Define GM's strategy for AI safety standards, engage externally to influence evolving standards, and contribute to internal and external thought leadership. - Support regulatory rulemaking and policy responses related to AI safety-critical systems. - Establish an assurance plan and process to evaluate AI-related safety case evidence and verify that sufficiency criteria are met. - Provide AI expertise and safety guidance across Global Product Safety, Systems, and Certification activities. - Identify and drive opportunities to improve the efficiency and quality of safety work through the application of AI methodologies. - Mentor and develop team members, fostering a culture of technical excellence and continuous learning. Required Qualifications: - Bachelor's degree in Computer Science, Electrical Engineering, Mathematics, Physics, or a related field; or equivalent practical experience. - 10+ years of experience in AI/ML, engineering or a related field. - 5+ years in autonomous vehicles, robotics or related field. - Extensive experience in building large-scale models with significant focus on E2E validation. - Experience using LLMs, Generative AI, RAG, Deep learning, Reinforcement Learning, NLP, SVM, XGBoost, Random Forest, Decision Trees, Clustering. - Understanding of ISO/PAS 8800, NIST AI Risk Management Framework, EU AI Act, and other industry safety standards. - Proficiency in Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQL. - Experience with Cloud & Big Data Platforms (Azure, AWS, or GCP). - Experience with Deployment & MLOps tools (MLflow, Docker, Kubernetes, GitHub). - Excellent communication and collaboration skills.