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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Technical Specialist, ML Data - **Company:** Waymo LLC - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $159,000.0 - $202,000.0 - **Contract:** Permanent contract - **Skills:** Data Analysis, Data Visualization, Software Debugging, Machine Learning, Azure Machine Learning, Software Engineering, Data Processing, Scripting, Large Language Models, Prompt Engineering, Model Validation, Machine Learning Operations, Data Pipelines, Data Selection - **Published:** July 18, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/86478910/1 ## About the Role * 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. ## 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 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 ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [How Machine Learning is turning the Automotive Industry upside down](https://www.wearedevelopers.com/videos/61-how-machine-learning-is-turning-the-automotive-industry-upside-down) - [JavaScript? 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