Safety Research Engineer - Quantitative Risk Assessment

Zoox

2600

Foster City, CA, United States

Posted on: 2025-07-15

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 on an ambitious journey to develop a full-stack autonomous mobility solution for cities and safely deploy such a robotaxi solution. Zoox’s System Design and Mission Assurance (SDMA) team is responsible for constructing the safety case for each milestone. We play a foundational role for the success of the company. As a safety research engineer focused on quantitative risk assessment, you will help Zoox to improve and expand our existing safety risk assessment framework. The safety risk assessment framework is a critical part of the overall safety case and informs the decision-making in every aspect of the technology.

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:

Develop and implement methodologies and data pipelines to evaluate the efficacy of human operators and the robustness of off-robot software and hardware systems. Establish a framework for incorporating field and service data into predictive metrics.

Collaborate cross-functionally with engineering teams to analyze component-level validation results and their impact on overall system performance.

Drive continuous improvements to achieve performance requirements for human operators and cloud software.

Contribute to the improvement and evolution of the Safety Case of Zoox technology, in close collaboration with cross-functional teams including Software, Hardware, Vehicle Development, Fleet Operations, Safety Strategy and Operations, Legal, etc.