Job description

The Product Security team is responsible for conducting structured security analyses of Zoox products and applying security standards judiciously throughout the System Development Life Cycle at Zoox. The ideal candidate for this team will have a robust background in systems engineering, coupled with a demonstrated passion and expertise in cybersecurity. In this role, you will: Perform Risk Assessment by quantifying security related safety risks, collaborating with cross functional teams particularly safety teams to ensure alignment with safety and security objectives while supporting risk-informed engineering decisions. Drive verification of quantified risks by fuzzing real world scenarios in simulator environments. Write design proposals and drive execution while proactively communicating risks to partner teams. Proactively identify opportunities to roll up risk metrics into various company wide frameworks. Interface with a multitude of engineering teams within Product Security as well as across software and hardware engineering. Build and maintain a strong understanding of the underlying engineering constraints and factor that understanding into the analysis and recommendations. Qualifications Master’s degree in CS or related engineering field (software, hardware, systems) and 7+ years of experience A strong general systems engineering background along with a demonstrated passion and concrete expertise in cybersecurity Prior experience with quantitative risk assessment frameworks (eg: EPSS, Attack trees/graph quantification, Monte Carlo simulations, Bayesian networks) Prior experience with the risk analysis of complex embedded systems and a demonstrated skill in turning the analysis into high-quality written deliverables Pragmatic adoption of AI/LLM toolchains for security analysis and authoring high quality regulatory work products. Bonus Qualifications Experience with autonomous vehicle software stack (e.g., Perception, Prediction, Planning) Experience implementing large-scale evaluation pipelines Familiarity with common cloud deployment architecture and frameworks 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. Follow us on LinkedIn Accommodations If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com 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.

Requirements

Must-haves

Drive verification of quantified risks by fuzzing real world scenarios in simulator environments.
Write design proposals and drive execution while proactively communicating risks to partner teams.
Proactively identify opportunities to roll up risk metrics into various company wide frameworks.
Master’s degree in CS or related engineering field (software, hardware, systems) and 7+ years of experience
A strong general systems engineering background along with a demonstrated passion and concrete expertise in cybersecurity
Prior experience with quantitative risk assessment frameworks (eg: EPSS, Attack trees/graph quantification, Monte Carlo simulations, Bayesian networks)
Prior experience with the risk analysis of complex embedded systems and a demonstrated skill in turning the analysis into high-quality written deliverables
Pragmatic adoption of AI/LLM toolchains for security analysis and authoring high quality regulatory work products.
Experience with autonomous vehicle software stack (e.g., Perception, Prediction, Planning)
Experience implementing large-scale evaluation pipelines
Familiarity with common cloud deployment architecture and frameworks

Nice-to-haves

Bonus Qualifications

Application prep

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Keywords to mirror

aillmmachine learning

Platform: lever
Effort: quick