Senior/Staff Safety Research Engineer - Quantitative Risk Assessment
AI Summary
A Senior/Staff Safety Research Engineer leads quantitative risk assessment to improve Zoox’s safety framework, analyzes large-scale driving and testing data, and standardizes safety practices across cross-functional teams.
About this role
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.
In this role, you will:
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Lead efforts to improve the fidelity of Zoox’s safety and progress performance metrics
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Apply distributed computing algorithms to efficiently analyze petabytes of urban driving and vehicle testing data
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Develop and standardize best practices of safety risk assessment across the company
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Develop and automate the tooling of the the risk assessment framework
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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.
Qualifications
M.S. or higher degree in an Engineering or Science discipline with a strong focus on Statistics, Probability Theory, or Data Science
Proficiency in quantitative analysis/modeling tools
Proficient with SQL / Spark / Python for interfacing with Zoox’s urban driving and simulation data
At least 3 years of relevant work experience in understanding and quantifying safety risks
Collaborative team player with strong written and in-person communication skills
Bonus Qualifications
Ph.D. in an Engineering or Science discipline with a strong Data Science or Statistics focus
Publications in the field of quantitative risk assessment for safety engineering
Experience with human behavior data
Skills
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