The opportunity
Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to…
What you'll do
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.
What they're looking for
- 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).
