Safety Engineer - Risk Management

The Waymo Safety team works to promote and help to continuously improve the safety of Waymo's fully autonomous driving technology. Our experts develop safety goals and strategies, and conduct safety engineering analyses to ensure safety is being considered throughout the design and development of our vehicles. The team develops and promotes safety strategies and policies for autonomous vehicles for its work with regulatory authorities, lawmakers, law enforcement and public and non-profit organizations. Our Safety Team also helps advise on compliance with applicable environmental, health, and safety regulations.

In this hybrid role, you will report to Risk Management, Safety Engineer

You will:

• You will architect best practices for risk assessment methodologies, including publishing internal guidance on risk model, data sources and computation methods.
• You will partner with domain experts from systems, hardware, software, and operations to build robust risk assessments with sustainable overhead for execution and flexibility in the face of an expanding product scope.
• You will help define what test coverage and field monitoring is necessary to ensure regression protection.
• You will identify stakeholders, learn their pain points, and work to unblock barriers to scale by streamlining rate limiting processes, automating repetitive tasks, and developing tools to enable future projects. You continously evaluate the tools available inside and outside the team/company/industry, using the right ones to unlock increased scale without compromising on rigor.
• You will translate complex probabilistic risk models and statistical analyses into clear, actionable safety arguments and executive summaries for technical leaders, cross-functional partners, and safety review boards.
• You will champion and promote a robust safety culture and the continuous improvement of the Waymo safety program across the engineering and operations organizations.

You have:

• An advanced degree in Computer Science, Robotics, Engineering or other relevant technical field OR 4 years of practical experience involving safety risk assessments and quantitative statistical methods.
• Proven experience developing quantitative risk models, Probabilistic Risk Assessments (PRA), or reliability analyses using statistical and mathematical techniques (e.g., Monte Carlo simulations, uncertainty modeling, distribution fitting).
• Proficiency in Python, R, or SQL for data manipulation, statistical evaluation, and building automated risk analysis scripts (focusing on data analysis and modeling rather than production software infrastructure).
• Strong foundation in the process of risk management, including the end-to-end lifecycle of risk: identification, assessment, mitigation and acceptance of risk (e.g. following ISO 31000/31010 or equivalent standards).
• Solid understanding of safety critical system designs and the systems engineering "Verification and Validation" process.
• Demonstrated technical project management skills with the ability to effectively manage multiple parallel, cross-functional safety-critical projects and teams. Ability to thrive in ambiguity.

We prefer:

• Deep background in data science and statistics with a focus on analyzing large data sets for quantitative risk reduction, safety insights, and performance assessment.
• Experience performing system safety risk management in autonomous vehicles, aerospace, defense, robotics, rail, or medical device industries.
• Experience mentoring or training engineering teams on quantitative risk assessment methods and safety principles.
• Experience with C++ and Python.
• Success as a safety culture champion, influencing engineering culture, ensuring safety is a first-class citizen in every design discussion.
• Proven leadership experience in working with external partners, contractors, suppliers, and regulatory bodies/auditors.