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Zoox is developing the first ground-up, fully autonomous vehicle fleet and supporting ecosystem. This Manager role leads a high-performing team of Data Scientists, ML Validation Engineers, and Software Engineers focused on model evaluation and validation for autonomous systems.
Key Responsibilities:
- Lead, mentor, and scale a team while driving roadmaps, sprint execution, resource allocation, and high-throughput model releases with safety guardrails
- Foster a culture of statistical excellence, healthy skepticism, proactive risk tracking, and data-driven decision-making
- Define and execute end-to-end validation strategies across offline evaluation, open/closed-loop simulation, and shadow-mode fleet benchmarking
- 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
- Partner with Planner, Prediction, MLOps, and Developer Efficiency teams to translate behavioral requirements into measurable validation targets and optimize evaluation pipelines
- Translate complex model performance trade-offs, statistical uncertainty, and safety risks into clear, data-driven recommendations for release decisions and executive leadership
Requirements:
- Master's or PhD in Computer Science, Robotics, Applied Statistics, or related field
- 3+ years of direct engineering management experience leading Data Science, Machine Learning, or V&V engineering teams
- 7+ years of technical experience in robotics, autonomous systems, or AI/ML
- Strong background in ML model validation, behavioral evaluation frameworks, system-level performance benchmarking, and statistics
- Strong technical foundation in Python and modern data/ML platforms
- 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
- Proven familiarity with modern C++/Python ML environments, simulation frameworks, and high-throughput ML evaluation pipelines
- Demonstrated ability to navigate complex organizational trade-offs between release velocity, compute cost, and safety rigor
Bonus Qualifications:
- Experience with autonomous vehicles, robotics, or other safety-critical systems
- Experience building large-scale simulation, model evaluation, or validation infrastructure
- Experience with reinforcement learning, generative AI, or distributed ML systems
