Job description

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. 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 Technical Specialist 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. The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process. Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements. Salary Range $159,000 — $202,000 USD

Requirements

Must-haves

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.
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
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlmachine learning

Platform: greenhouse
Effort: quick