Behaviour Labeling Policy Program Manager, Labeling Operations

<div class="content-intro"><p>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.</p></div><p>The Labeling Data Program Org owns the execution and creation of curated labeled datasets which are critical for training and evaluation of ML models that power the Waymo Driver.</p> <p>As a Program Manager in this team, you will be the operational backbone of our machine learning initiatives. You will own and drive the complex, cross-functional programs that deliver high-quality data—the lifeblood of our models. You will orchestrate the end-to-end data lifecycle, from defining policy for new datasets and tooling to scaling data pipelines and ensuring our ML teams have the resources they need to innovate. This is a high-impact role for a technical, detail-oriented person, who is hands on and who thrives on turning ambiguous data needs into tangible, scalable solutions.</p> <p> </p> <p><strong>You will:</strong></p> <ul> <li><strong>Behaviour Policy development:</strong> Be hands-on and spearhead ML data annotation policies, guidelines, and SOPs by collaborating with Engineering, Product, and Program teams</li> <li><strong>Operational Execution:</strong> Operationalize workflows with vendor partners, serving as the primary link between AI/ML development and day-to-day execution</li> <li><strong>Quality Management:</strong> Create golden datasets for evaluation of policy and processes used for data creation. Design and implement robust quality control processes and meta-quality checks for both human and AI-generated outputs</li> <li><strong>Subject matter expertise:</strong> Act as the Subject Matter Expert for annotation policy, providing technical leadership and consultation to cross-functional stakeholders, on your functional area</li> <li><strong>Process optimization:</strong> Identify tool issues, define technical requirements for Engineering, and drive user testing for new features and advancements</li> </ul> <p><strong>You have:</strong></p> <ul> <li><strong>3+ years of experience</strong> in product data labeling operations within the autonomous driving industry (or a degree from a top-tier institute)</li> <li><strong>Hands-on expertise in ML data annotation</strong>, including developing labeling policies, managing "Human-in-the-Loop" programs, and analyzing quality outcomes</li> <li><strong>Deep understanding of ML development and evaluation</strong>, specifically regarding dataset quality assurance, bias detection, and statistical performance metrics</li> <li>Ability to learn, quickly master new concepts and proprietary software tools. Knowledge of US driving rules and regulations is an added advantage</li> <li><strong>Strong interpersonal and communication skills</strong> to effectively collaborate across diverse teams in a fast-paced environment</li> </ul> <p><strong>We prefer:</strong></p> <ul> <li>Hands on experience of managing data annotation or driving data analysis for an autonomous driving company</li> <li>Demonstrated strong execution with ability to drive outcomes </li> <li>Basic SQL querying and other analytics tools</li> <li>Competency in LLM / AI based agentification of process and / or ML for robotics domain experience</li> <li>A greater focus on using your subject matter expertise for results analysis and direct customer consultation in the development of new and improved s ... (truncated, view full listing at source)