Zoox

Fully electric autonomous ride-hailing service platform

Manager – Model Evaluation

Robotics & Autonomous SystemsConfirmed live in the last 24 hours

Full-timeFoster City, CA, USA$237k - $338k /yrHybrid

About the Job

Requirements

A Master's or PhD in Computer Science, Robotics, Applied Statistics, or a related field.
At least 3 years of direct engineering management experience leading Data Science, Machine Learning, or V&V engineering teams.
At least 7 years of technical experience in robotics, autonomous systems, or AI/ML.
A strong background in ML model validation, behavioral evaluation frameworks, system-level performance benchmarking, and statistics.
A 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).
Ability to partner with systems software engineers, review technical architecture, and understand compute/performance trade-offs.
A track record of leading teams evaluating complex robotic systems.
Familiarity with modern C++/Python ML environments, simulation frameworks, and high-throughput ML evaluation pipelines.
Ability to navigate complex organizational trade-offs between release velocity, compute cost, and safety rigor.

Responsibilities

Lead, mentor, and scale a team of Data Scientists, ML Validation Engineers, and Software Engineers while driving roadmaps, sprint execution, resource allocation, and high-throughput model releases with rigorous 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 to ensure robust behavioral model performance.
Translate statistical uncertainties and regressions into clear, data-driven recommendations for release gating and executive leadership.
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 while fostering statistical rigor and proactive risk management.
Partner closely with Planner, Prediction, MLOps, and Developer Efficiency teams to translate behavioral requirements into measurable validation targets, streamline dataset and evaluation pipelines, and optimize runtime and compute costs.
Translate complex model performance trade-offs, statistical uncertainty, regressions, and safety risks into clear, data-driven recommendations for release decisions and executive leadership.

Desired 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.

Salary Range: 237000 to 338000 (Currency: USD) (Pay period: per-year-salary)

As the Manager of Model Validation & Verification (VnV) for Behavior Autonomy, you will lead an engineering and data science team responsible for evaluating, benchmarking, and validating the machine learning models and behavioral algorithms that drive our autonomous vehicle (AV) prediction and planning stacks.

You will own the statistical frameworks, offline/online evaluation metrics, and validation pipelines that ensure our behavioral models operate safely, comfortably, and predictably. Operating at the intersection of Data Science, Machine Learning, and Safety Engineering, you will partner closely with Autonomy Software, Prediction, Planner, and ML Operations teams to establish data driven release criteria for our AV fleet.

In this role, you will:

Team Leadership & Execution: Lead, mentor, and scale a high-performing team of Data Scientists, ML Validation Engineers, and Software Engineers while driving roadmaps, sprint execution, resource allocation, and high-throughput model releases with rigorous safety guardrails. Culture of Rigor: Foster a culture of statistical excellence, healthy skepticism, proactive risk tracking, and data-driven decision-making.

Validation Strategy & Methodologies: 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. Statistical uncertainties, and regressions into clear, data-driven recommendations for release gating and executive leadership.

Metrics, Release Gating & Rigor: 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 while fostering statistical rigor and proactive risk management.

Cross-Functional & Infrastructure Partnership: Partner closely with Planner, Prediction, MLOps, and Developer Efficiency teams to translate behavioral requirements into measurable validation targets, streamline dataset and evaluation pipelines, and optimize runtime and compute costs.

Executive Communication & Decision-Making: Translate complex model performance trade-offs, statistical uncertainty, regressions, and safety risks into clear, data-driven recommendations for release decisions and executive leadership.

Qualifications:

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.

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

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.

Bonus Qualification:

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.

About Zoox

Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.

Accommodations

If you need an accommodation to participate in the application or interview process please reach out to [email protected] or your assigned recruiter.

A Final Note:

You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.

About the Company

Zoox

Fully electric autonomous ride-hailing service platform

Zoox designs and operates an autonomous ride-hailing service built around a purpose-built, fully electric vehicle designed specifically for autonomous operation. The vehicle uses advanced sensors, cameras, and AI to navigate complex urban environments, offering a four-passenger interior for comfort. Unlike competitors that retrofit existing cars, Zoox engineers both the vehicle and the service from the ground up, enabling optimized safety, efficiency, and passenger experience through integrated software that optimizes routing and reduces wait times. The company emphasizes safety through extensive testing and regulatory collaboration, and sustainability via electric mobility. The ultimate goal is to provide a smarter, safer, and more efficient urban transportation system that improves city life while reducing emissions.

Company size: 5,001-10,000
Company stage: Acquired
Total funding: $1.2B
Headquarters: Menlo Park, CA
Founded: 2014

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