Model Evaluation Manager

Join Zoox, an autonomous vehicle company, as the Model Evaluation Manager. In this role, you will lead a team responsible for evaluating, benchmarking, and validating machine learning models and behavioral algorithms. You will establish data-driven release criteria for the AV fleet and foster a culture of statistical excellence. The position offers a range of benefits, including paid parental leave, health insurance, and work-from-home opportunities.

• Lead and mentor a high-performing team of Data Scientists, ML Validation Engineers, and Software Engineers, driving roadmaps, sprint execution, resource allocation, and high-throughput model releases.
• Define and execute end-to-end validation strategies across offline evaluation, open/closed-loop simulation, and shadow-mode fleet benchmarking to ensure robust behavioral model performance.
• Oversee metric development and standardization with System Safety and Autonomy teams, establishing quantitative go/no-go release criteria for Behavioral Planner and Prediction ML models.

• Software & Systems Literacy: Strong technical foundation in Python and modern data/ML platforms, with exposure to or conceptual literacy in large-scale production codebases (C++ or distributed systems). Proven ability to partner with systems software engineers, review technical architecture, and understand compute/performance trade-offs, Track record of leading teams evaluating complex robotic systems
• Experience: Masters or PhD in CS, Robotics, Applied Statistics or a related field and 3+ years of direct engineering management experience leading Data Science, Machine Learning, or V&V engineering teams, alongside 7+ years of technical experience in robotics, autonomous systems, or AI/ML
• Domain Knowledge: Strong background in ML model validation, behavioral evaluation frameworks, system-level performance benchmarking, and statistics
• Technical Depth: Proven familiarity with modern C++/Python ML environments, simulation frameworks, high-throughput ML evaluation pipelines
• Cross-Functional Leadership: Demonstrated ability to navigate complex organizational trade offs between release velocity, compute cost, and safety rigor
• Experience with reinforcement learning, generative AI, or distributed ML systems
• Experience building large-scale simulation, model evaluation, or validation infrastructure
• Experience with autonomous vehicles, robotics, or other safety-critical systems