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
De-risking New Deployments: Through meticulous triage of driving and simulated events, issue discovery, and continuous field monitoring, SWQOps provides early warnings and critical insights. This "early intervention in RO issue detection" ensures operational resilience and safety, particularly in new and complex environments, which is critical as Waymo enters multiple new cities and ramps up platforms like Ojai.
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.
Enabling Market Expansion: 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.
Data Flywheel: Supporting the development of a single, automated, end-to-end machine learning flywheel for the entire Waymo Driver. A successful flywheel will be 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.
Drive the strategy and technical implementation of decomposing complex safety: triage workflows. Develop and apply advanced operational and ML techniques to enable automation, ensuring scalability as mileage and geographic operations expand.
Serve as the subject matter expert for human-in-the-loop ML systems in the: safety problem space. Define and refine requirements for generating high-quality, dense, and broad human feedback to optimize ML models performance.
What they're looking for
- 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.
- Serve as the key link between AI/ML development and operational execution.: Author and drive the adoption of foundational technical policies and standards, strategic roadmaps, and process blueprints that enable the organization to scale and support stakeholder needs.
- Advise senior stakeholders on the long-term technical strategy and: operational capabilities of the organization, serving as a trusted partner for critical decisions.
