Principal AV Behavior Safety Engineering Lead

What you'll do

• Interpret standards like ISO 21448 and AVSC to define safety strategies for autonomous system development.
• Own the behavior-focused portion of the ADS Safety Case, including key claims and sufficiency criteria.
• Define the end-to-end validation methodology for AI-driven systems across simulation, closed-course, and public roads.
• Set strategies for systematically breaking down ODDs and validating performance per behavior and in aggregate.
• Develop safety performance indicators (SPIs) and human benchmarks for AV behaviors and overall system performance.
• Assess AV performance across safety and reliability dimensions using multi-source data to provide prioritized feedback to engineering.
• Run an assurance process to verify safety targets and sufficiency criteria to support launch readiness.

What we're looking for

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
• 8+ years of experience in machine learning, engineering, data science, or a related field.
• 8+ years of experience in autonomous vehicle or robotics development or a related field.
• Demonstrated experience working on production-intent AV programs.
• Track record of providing technical safety leadership in AV development, including safety strategies, risk assessments, and validation methodologies.
• Deep understanding of AV behavior development, including ODD definitions, behavior analysis, and evaluation criteria.
• Experience applying AV safety standards and best practices such as ISO 5083, ISO 21448 (SOTIF), and AVSC practices.
• Excellent communication skills and the ability to influence cross-functional teams without direct authority.
• Masters Degree in Computer Science, Engineering, Mathematics, or a related field (preferred).
• Proficiency in Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, and SQL (preferred).
• Experience with LLMs, Generative AI, RAG, Deep Learning, Reinforcement Learning, NLP, SVM, XGBoost, Random Forest, Decision Trees, and Clustering (preferred).
• Experience with Microsoft Azure, AWS, or Google Cloud Platform (preferred).
• Experience with MLflow, Model Monitoring & Versioning, Docker, Kubernetes, GitHub, and Jira (preferred).
• Proficiency in Tableau, PowerBI, Pandas, and NumPy (preferred).