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. 

In this role you will help build Waymo’s offboard Field Safety monitoring capabilities, which help to ensure the continued safety of Waymo’s vehicles as we expand our operations. As part of Waymo’s Field Safety team, you will be involved in the analysis of events and issues affecting Waymo’s autonomous vehicles. Inspired by these real-world issues, you will design and build proactive, offboard field monitors, making use of Waymo's latest available AI tools. You will receive direct mentorship from Waymo's Field Safety Architect and other safety leaders to transition cutting-edge academic theory into real-world autonomous fleet safety at scale.

This role follows a hybrid work schedule and reports to the Senior Director, Safety.

You will:

• Participate in investigations into driverless events, employee safety concerns, and other potential issues that could pose risks to the safety of Waymo vehicles, analyzing field data to identify trends.
• Identify opportunities for further offboard field monitoring, then design and build your own monitors, making use of Waymo's latest available AI tools.

You have:

• Ph.D. in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Computer Science, or a related technical field. This role does not require post-doctoral work experience, and recent Ph.D. graduates are highly encouraged to apply.
• Hands-on experience developing software for data analysis, system modeling, or simulation. Demonstrated ability to independently write clean, maintainable, and well-documented code (e.g., Python, C++) to build reusable data analysis tools that anticipate the next technical question.
• Solid grasp of one (and, ideally, multiple) fundamental areas relevant to autonomous vehicle safety including physical system modeling, control theory, vehicle dynamics, sensing, perception, planning, behavior prediction, and machine learning.
• A natural curiosity to explore novel ways of leveraging data to rapidly answer safety questions arising from field events.
• Eagerness to network internally and build relationships across diverse engineering teams to trace issues and collect data.
• Excellent written and verbal communication skills.

We prefer:

• Software experience in autonomy or machine learning.
• Familiarity with statistical data analysis, quantitative risk assessment, or reliability engineering.
• Experience working with large-scale datasets, database querying (SQL-like), or building interactive offboard data dashboards.