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
Support the development of a single, automated, end-to-end machine learning: flywheel for the entire Waymo Driver: the core engine for scaling our technology, enabling faster ODD expansion, quicker remediation of driving issues, and a significant reduction in the engineering effort required to maintain and improve the driver.
De-risking New Deployments: Through triage of driving events, issue discovery, and field monitoring, SWQOps provides early warnings and critical insights. This "early intervention in RO issue detection" ensures operational resilience and safety which is critical as Waymo enters multiple new cities and ramps up platforms like W12.
Enabling Market Expansion which will allow our team is deeply integrated into: every stage of Waymo's market entry framework, from initial city evaluation (OK2Plan) to scaling operations (OK2Scale). We provide the necessary data analysis, policy development, and quality assurance to unblock critical milestones, preventing slowdowns in market expansion velocity.
Driving Engineering Velocity: By handling the vital work of performance evaluation, issue deep-dives, and data set curation, SWQOps collaborates heavily and allows Waymo's Engineering, SysEng, Simulation, and Data Science teams to focus on their core tasks of developing and improving the Waymo Driver.
Partner with Engineering to design, test, and deploy cutting-edge Machine: Learning (ML) and Generative AI (Gen-AI) models and tools to drive step-change improvements in issue discovery & detection, triage efficiency, and quality assurance.
Leverage AI-powered insights and traditional triage signals to proactively: identify emerging on-road issue trends, new risk scenarios, and edge cases. Develop and refine data-driven strategies for issue discovery and monitoring, enhanced by ML model outputs
What they're looking for
- Be a main link between AI/ML development and operational execution. Define: and document new policies, and Standard Operating Procedures (SOPs) that integrate AI tools and insights into daily vendor workflows.
- Design and implement quality control processes for both human and: AI-generated outputs. Perform meta-quality checks, validate the integrity of vendor work, and provide feedback to improve both human and model performance.
- Be the subject matter expert for our Software Quality Operations, working: with partners, program lead, and vendor teams to ensure seamless adoption and maximum impact of AI/ML advancements in our quality processes. Be the trusted source for creating and updating technical guidelines, and standard operating procedures for new scopes, platforms, and driving signals
- Provide technical leadership and consultation to partners to enhance our: workflows and quality. You'll identify and escalate issues with our tools, providing technical requirements to engineering, and driving user testing to support the development and deployment of new tooling features.
