About the role
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 mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. As an L4 Policy & Quality Specialist, you will serve as the Mountain View operational backbone of the Labeling Policy Program. You will play a key role in accelerating MTV Perception Engineering velocity by translating complex machine learning data
requirements into consistent, high-quality, and scalable labeling policies. Operating in the same time zone as our core engineering partners, you will drive rapid policy iteration, create critical golden datasets to enable fast labeling queue setup. This is an execution-focused role for a technical, detail-oriented specialist who thrives on driving clarity, alignment, and operational excellence in labeling workflows. You will: Translate
requirements to policies: Collaborate directly with MTV-based Perception Engineers/ ML model owners to understand their specific data goals, translate their ambiguous machine learning
requirements into precise labeling instructions, and publish clear, actionable labeling policies (with support from the HYD Policy Specialist and vendor partners). Drive queue readiness & golden datasets: Speed up the initial labeling queue setup process by executing rapid policy iterations and hand-crafting golden datasets (small-scale baseline datasets of 10s of examples) to establish quality baselines before launching full-scale operations. Direct vendor teams: Provide technical guidance and operational direction to vendor labeling experts to enable rapid policy setup and ensure that the ~10 active labeling queues under your purview run smoothly and meet safety and performance objectives. Address edge cases & regional nuances: Provide critical, detailed inputs on long-tail edge cases and coordinate with regional country specialists to ensure country-specific driving rules and local nuances are accurately captured and validated, ahead of Waymo's deployment in these new countries. Enable quality and process improvements: Monitor labeling pipelines, conduct targeted technical analyses to identify data quality trends, and build/maintain automated data analysis tools to proactively identify improvements in the broader labeling workflow. Facilitate cross-functional knowledge sharing: Act as the primary technical interface between requesters and operations, ensuring on-ground dissipation of policies, managing policy amendments, and resolving complex escalations from requestors or vendor teams. You have: 4-5+ years of experience in data analysis, operations, or program management with a focus on machine learning data annotation, taxonomy design, or human-in-the-loop workflows. Operational project management: Demonstrated ability to work independently on operational workflows and successfully project manage small sub-working groups or vendor squads. Core ML data lifecycle understanding: Practical knowledge of dataset curation, labeling pipelines, data quality control metrics, and baseline model evaluation concepts. Analytical aptitude: Experience conducting technical data analyses using pre-established tools (or building simple automation scripts) to diagnose pipeline issues, track vendor quality, and generate actionable insights. Adaptable & detail-oriented: Comfort working within a dynamic environment, translating vague technical needs into clear documentation, and maintaining a high standard of attention to detail. We prefer: Experience with scripting languages (e.g., Python, SQL) or basic automation techniques to parse high volumes of critical data. Experience collaborating with software engineering stakeholders to gather structured