The Software Quality Operations (SWQOps) team is at the heart of ensuring the safety, reliability, and quality of the Waymo Driver. Our mission is to build an adaptable and scalable operation, increasingly powered by AI, to deliver the crucial insights necessary to confidently deploy and grow Waymo's autonomous vehicle service.

Why This Team is Essential to Waymo's Success:

Waymo is undergoing unprecedented growth, rapidly expanding into new cities (targeting ~20 new cities by EOY 2026) and launching new vehicle platforms. SWQ Ops plays a critical role in this expansion, making it possible to scale safely and efficiently. The Safety Operations team within SWQ Ops owns the scale delivery of datasets and methodologies used to evaluate the Safety and performance of the driver as Waymo continues to scale. We are on the front lines of:

• 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.

You will:

• 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.
• 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.

You have:

• BS/BA degree or 7+ years of relevant work experience in AV Software Quality Operations / ML Operations
• Proven ability to manage complex, technical projects and experience working across technical partners (Product, Engineering, Data Science, Systems Engineering) to drive outcomes.
• Increased competency in supporting all phases of the machine learning development lifecycle, from data preparation and training to validation, deployment, and continuous monitoring.
• Experience with ML testing and validation, including dataset quality assurance, bias detection, edge-case scenario testing, and performance evaluation using statistical metrics.
• Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations.
• A proven ability to work in a fast-paced, high-stress environment while maintaining good judgment
• Excellent communication and interpersonal skills to effectively collaborate with a wide range of individuals in a diverse and dynamic work environment.

We prefer:

• Undergraduate in technical degree preferred.
• Experience within A / V space.
• Competency in LLM / transformer models, and / or ML for robotics domain experience.
• Competency in SQL querying / data analysis.
• Experience working with offshore teams / multiple local operations hubs.

Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process.

The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.

Waymo employees are also eligible to participate in Waymo's discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.

Salary Range

$190,000-$234,000 USD