The opportunity
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…
What you'll do
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
+ years of experience in data analysis, including identifying trends,: generating summary statistics, and drawing insights from quantitative and qualitative data.
What they're looking for
- 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.