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.

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 requirements 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 leader who thrives on turning ambiguous data needs into tangible, scalable solutions.

You will:

• Drive the ML Flywheel: Lead the end-to-end lifecycle of ML data, from initial mining and curation to labeling policy definition, validation, and model evaluation. Work cross-functionally to ensure coordination and alignment on objectives and key results.
• Translate Policy to Code:  Lead the development of sophisticated labeling policies for complex AV domains (e.g., behavior prediction, long-tail edge cases). Convert ambiguous ML quality problems into precise, scalable annotation policies and data taxonomies. 
• Build Evals & Metrics: Design and implement ML evaluation frameworks. Identify key data-centric drivers of model performance and create the metrics that track ML quality at the data level.
• Cross-Functional Leadership: Communicate effectively with technical and non-technical audiences at various levels of seniority, including producing analytical write-ups, dashboards, and data visualizations to convey your findings and recommendations to our team and cross-functional stakeholders
• Influence ML data selection strategies (active learning, hard-mining) to ensure we are labeling the most impactful data to maximize ROI from the labeling effort

You have:

• 8+ years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
• Deep understanding of the ML data lifecycle: labeling, taxonomy design, quality control, and data curation.
• Experience with ML Data Flywheel and working understanding of ML development life cycle (e.g., model deployment,model evaluation, data processing, debugging, fine tuning).
• Background in leading and managing complex programs that span across organizations and functions, with specific experience in Machine Learning data annotation or Human-in-the-Loop initiatives.
• Strong ability to thrive in a dynamic environment, demonstrating comfort and effectiveness when dealing with ambiguity.
• Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations.

We prefer:

• Experience with scripting language, machine learning tools, techniques and systems (including prompt engineering and fine-tuning LLMs ) is a strong plus.
• Demonstrated ability to extract, manipulate, and apply machine learning techniques to high volumes of critical, product-related data.
• Demonstrated ability in working with a variety of engineering stakeholders to gather requirements, explain models, and iterate to make improvements.
• Excellent written and verbal communication and ability to describe technical implementations or analyses to a non-tech audience in an effective manner.
• Excellent problem-solving and critical thinking skills with attention to detail in an ever-changing environment.
• A greater focus on using your subject matter expertise for results analysis and direct customer consultation in the development of new and improved. solutions.