Field Reliability Analyst

zoox

4100

Foster City, CA, USA

Posted on: 2026-01-28

Category: emobility

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Employment type:

Full time

Experience required:

Intermediate

Salary

Salary not provided

About the company:

Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.

Zoox is transforming mobility with fully autonomous, electric vehicles designed from the ground up for a driverless future. Our mission is to make transportation safer, more sustainable, and accessible to everyone. At Zoox, innovation, collaboration, and a bold vision for the future drive everything we do.

The Reliability team leverages data from our autonomous vehicle fleet and operations to understand system performance, identify failure trends, and improve vehicle availability and safety. As a Reliability Data Analyst, you will work closely with Reliability Engineering, Mission Assurance, and Vehicle Systems teams to develop analytics, metrics, and tooling that support reliability targets and continuous improvement across our autonomous platform. This role is ideal for someone early in their career who enjoys hands-on data work, statistical analysis, and collaborating with engineers to translate vehicle and operational data into actionable reliability insights. You will contribute directly to production decisions such as issue prioritization, design improvements, and maintenance strategies while building depth in reliability engineering and large-scale data pipelines. You’ll join a diverse, experienced reliability organization and gain access to one of the most unique vehicle datasets in the autonomous driving industry.

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.

In this role, you will:

Analyze fleet, vehicle, and operational data to support reliability, safety validation, and service readiness initiatives

Partner with Reliability Engineering and Mission Assurance to help define and track key performance metrics

Build and scale end-to-end predictive frameworks to monitor component/subsystem health, degradation trends, and real-world usage patterns.

Build and maintain data pipelines, queries, and dashboards to enable visibility into system health and performance

Perform statistical analyses to identify trends, anomalies, and improvement opportunities across hardware and software systems

Support investigations into vehicle issues by cleaning, aggregating, and visualizing large datasets

Contribute to best practices around data quality, reproducibility, and analytical rigor

Communicate findings clearly to technical stakeholders through written summaries, visualizations, and presentations

Qualifications:

1–3 years of professional experience (or equivalent academic/project experience) in data science, analytics, reliability engineering, or a related field

Working proficiency in SQL for querying relational databases

Working proficiency in Python and common data analysis libraries (Pandas, NumPy, SciPy)

Foundational understanding of statistics and the ability to communicate uncertainty and assumptions

Experience building analyses, metrics, or dashboards to support engineering or business decisions

Familiarity with version control systems (Git).

General knowledge of physics and engineering principles.

Strong communication skills and ability to collaborate across engineering teams