Safety Engineer - Risk Management

Job Summary

The Waymo Safety Team is seeking a hybrid Risk Management, Safety Engineer to architect best practices for risk assessment methodologies, collaborate with domain experts, and streamline processes.

Responsibilities

• Architect best practices for risk assessment methodologies, including publishing internal guidance on risk models, data sources, and computation methods.
• Collaborate with domain experts to build robust risk assessments with sustainable overhead for execution and flexibility in the face of an expanding product scope.
• Work with domain experts to define what test coverage is needed to support ongoing risk assessments with regression protection and field monitoring.
• Connect with stakeholders, learn their pain points, and work to unblock barriers to scale.
• Streamline rate limiting processes, automate repetitive tasks, and develop tools to enable future projects.
• Familiarize yourself with tools available inside and outside the team/company/industry to unlock increased scale without compromising on rigor.
• Champion and promote a robust safety culture and the continuous improvement of the Waymo safety program across the engineering and operations organizations.

Requirements

• 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.
• Experience in software engineering for simulation and data evaluation, using Python or other high level language (e.g. SQL, R, C++).
• Direct experience developing risk models, sourcing and validating data, and using findings to make decisions/recommendations.
• A 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 or equivalent standards).
• A comprehensive understanding of safety critical system design 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.

Preferred Qualifications

• Advanced skills in data science and statistics with a focus on analyzing large data sets for quantitative risk reduction, safety insights, and performance assessment.
• Experience building automated, scalable data pipelines and working in production software environments.
• Experience in the role of “Trainer” of quantitative risk assessment methods for a variety of engineering backgrounds.
• Experience with C++ and Python.
• Proven leadership experience in working with external partners, contractors, suppliers, and regulatory bodies/auditors.