Policy & Quality Specialist (ML Perception Data)

Join Waymo as a Policy & Quality Specialist, where you will play a crucial role in accelerating the velocity of Perception Engineering. You will translate complex machine learning data requirements into clear labeling policies, drive rapid policy iteration, and create critical golden datasets. You will also provide technical guidance to vendor labeling experts, address edge cases and regional nuances, enable quality and process improvements, and facilitate cross-functional knowledge sharing. This is an execution-focused role for a detail-oriented specialist who thrives on driving clarity, alignment, and operational excellence in labeling workflows.

• Collaborer directement avec les ingénieurs en perception basés à Mountain View et les propriétaires de modèles d'apprentissage automatique pour comprendre leurs objectifs de données spécifiques et traduire leurs exigences en instructions de marquage précises.
• Accélérer le processus de configuration initiale de la file d'attente de marquage en exécutant des itérations rapides de politiques et en créant des ensembles de données dorées.
• Fournir des conseils techniques et une direction opérationnelle aux experts en marquage des fournisseurs pour permettre une configuration rapide des politiques.
• Operational project management: Demonstrated ability to work independently on operational workflows and successfully project manage small sub-working groups or vendor squads
• 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
• 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
• 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
• Core ML data lifecycle understanding: Practical knowledge of dataset curation, labeling pipelines, data quality control metrics, and baseline model evaluation concepts
• Experience with scripting languages (e.g., Python, SQL) or basic automation techniques to parse high volumes of critical data
• Prior experience working across multiple geographic locations and managing vendor-hosted operations
• Experience collaborating with software engineering stakeholders to gather structured requirements and explain technical operational policies
• Familiarity with prompt engineering and evaluation of model outputs (AI-generated workflows) is a plus