Principal Engineer, Data Quality, AI Foundry

Google•San Jose, CA

1d•$307,000 - $427,000

About The Position

The AI Foundry team's mission is to power AI journeys with useful and trusted data at scale, supporting Google products including Gemini model development and Google Search. The Data Quality team advances data quality to improve the user experience of Google's products. The Principal Engineer serves as the technical lead for data quality, setting the area's technical direction, vision, and strategy. The role is responsible for delivering high-quality datasets for Search and DeepMind training by coordinating upstream capabilities to meet downstream consumption requirements. It offers the opportunity to influence how Google understands and uses data at scale. The Core team builds and maintains technical foundations behind Google's products, including developer platforms, product components, and infrastructure. It develops central solutions, addresses technical barriers, and strengthens systems across Google.

Requirements

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 a technology organization.
7 years of experience in a technical leadership role, with or 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.

Nice To Haves

Master's degree or PhD in Computer Science, Artificial Intelligence, or a related field.
Experience in Search or Generative AI training data journeys, or experience with product data needs involving comparable journeys.
Experience defining and tracking complex metrics across large-scale and dynamic ecosystems.
Experience collaborating with high-level research teams, such as DeepMind researchers, to translate abstract data needs into scalable engineering solutions.
Track record of establishing data flywheels or self-healing data systems.

Responsibilities

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 a self-correcting and self-healing data flywheel for ML training data and Search Context as data moves through dynamic, large-scale systems, including sourcing, acquisition, and indexing, to the data store.
Collaborate with DeepMind and Search researchers, data analysts, and engineers to identify key Value of Data (VoD) signals.
Partner with AI Foundry infrastructure owners to evolve systems—including crawl, processing, and signal enrichment—to deliver high-quality datasets for Search features and AI models.
Provide technical guidance to critical components of the context quality area, including large-scale signal development and the design and development of quality metrics.