Principal Engineer, Data Quality, AI Foundry

Lead the strategy, technical vision, and roadmaps for the data quality team to deliver high-quality datasets for DeepMind and Search.Drive the architecture toward establishing the data flywheel for ML training data and Search Context that can self correct and self heal as the data travels through multiple dynamic and large-scale systems (such as sourcing, acquisition and indexing) to the data store.Collaborate closely with DeepMind and Search researchers, data analysts, and engineers to identify key Value of Data (VoD) signals.Partner with infrastructure owners in AI Foundry to evolve systems—including crawl, processing, and signal enrichment—to deliver high-quality datasets that power Search features and AI models.Provide technical guidance to critical components of the context quality area, such as large-scale signal developments, design and development of quality metrics.Minimum qualifications:Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Economics, a related technical field, or equivalent practical experience.15 years of experience in software engineering, building and working with systems in the technology organization.7 years of experience in a technical leadership role with/without direct reports.5 years of experience developing and deploying machine learning models on data sets.3 years of infrastructure or data systems experience building or managing infrastructure products, such as distributed storage systems, data warehousing, or data processing pipelines.Preferred qualifications:Master’s degree or PhD or in Computer Science, Artificial Intelligence, or a related field.Experience in Search or Generative AI training data journeys, or experience in product data needs with comparable journeys.Experience defining and tracking complex metrics across large-scale and dynamic ecosystems.Experience collaborating with high-level research teams (e.g., DeepMind Researchers) to translate abstract data needs into scalable engineering solutions.Track record of establishing "data flywheels" or self-healing data systems.The AI Foundry team's mission is to power AI journeys with useful and trusted data at scale, and powers various Google products including Gemini model development and Google Search. The Data Quality team in AI Foundry focuses on advancing data quality to enhance the user experience of Google’s products.We are seeking a Principal Engineer who will be the Technical Lead (TL) for data quality and set the technical direction, vision, and strategy for the area. In this role, you will be responsible for delivering high-quality datasets for Search and DeepMind training by orchestrating upstream capabilities to meet downstream consumption requirements. This is a unique opportunity to influence how Google understands and utilizes the world's data at an unprecedented scale.The Core team builds the technical foundation behind Google’s flagship products. We are owners and advocates for the underlying design elements, developer platforms, product components, and infrastructure at Google. These are the essential building blocks for excellent, safe, and coherent experiences for our users and drive the pace of innovation for every developer. We look across Google’s products to build central solutions, break down technical barriers and strengthen existing systems. As the Core team, we have a mandate and a unique opportunity to impact important technical decisions across the company.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $307000 - $427000 (USD) + 30% bonus target + equity + benefitsLearn more about benefits at Google.Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, Economics, a related technical field, or equivalent practical experience.15 years of experience in software engineering, building and working with systems in the technology organization.7 years of experience in a technical leadership role with/without direct reports.5 years of experience developing and deploying machine learning models on data sets.3 years of infrastructure or data systems experience building or managing infrastructure products, such as distributed storage systems, data warehousing, or data processing pipelines.