Waymo
Safety Engineer - Risk Management
Mountain View CA USA; San Francisco CA USAFrom $252kfull-timemidAdded today
About this role
Waymo seeks a Safety Engineer for Risk Management to architect and scale risk assessment methodologies for autonomous driving technology. You'll develop best practices, collaborate across teams to build robust risk models, and champion safety culture while managing multiple cross-functional projects.
What you'll do
- Architect and publish best practices for risk assessment methodologies, models, data sources, and computation methods
- Collaborate with domain experts to build and maintain robust risk assessments with sustainable execution overhead
- Define test coverage requirements to support risk assessments, regression protection, and field monitoring
- Streamline processes, automate repetitive tasks, and develop tools to enable scaling without compromising rigor
- Connect with stakeholders to identify pain points and unblock barriers to scale
- Champion and promote safety culture across engineering and operations organizations
What they're looking for
- Risk modeling and quantitative statistical methods
- Python or other high-level languages (SQL, R, C++)
- Software engineering for simulation and data evaluation
- ISO 31000 or equivalent risk management standards
- Safety-critical systems design and Verification & Validation processes
- Technical project management and cross-functional leadership
- Data science and large-scale data analysis
- Automated data pipeline development
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Waymo
Waymo develops autonomous driving technology and vehicles, building the AI systems, simulation platforms, and infrastructure that power the Waymo Driver. The company is hiring for ML infrastructure engineers, platform engineers, labeling system developers, backend software engineers, and automotive systems engineers to scale its autonomous driving capabilities.
- Website
- waymo.com
Likely interview questions
- Walk us through your experience developing and validating risk models—how did you ensure data quality and what decisions did your findings inform?
- How have you previously scaled risk assessment processes while maintaining rigor, and what tools or automation did you implement?