Staff Software Engineer, AI Foundry Data Quality and Training Data

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience designing, building, and operating large -scale distributed systems, with a focus on scalability, fault tolerance, and reliability.
• 2 years of experience in a people management, supervision/team leadership role.
• Experience working with algorithms and data structures, analysis, and software design.
  
• Experience in software engineering principles, design patterns, and system architecture.

Preferred qualifications:

• PhD degree in Computer Science, Machine Learning, Distributed Systems, Operations Research, or a related technical field.
• Experience with algorithms and mathematical or system performance improvements.
• Experience building and operating high-throughput distributed systems on infrastructure or equivalent batch and streaming frameworks.
• Experience as a tech lead manager leading engineering teams in domains bridging infrastructure and machine learning.
• Experience with data quality, dataset curation, value-of-data analysis, or training data pipelines for Large Language Models (LLMs) and multimodal foundation models.

About the job:

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

As a leader in the AI Foundry's Data Acquisition and Data Quality (DA&DQ) organization, you will guide the Data Quality team, a group central to Google's AI strategy. Your team will define the end-to-end strategy and execution for data quality, targeting intelligence, and evaluation for the strategic datasets that power DeepMind and Search. You will lead efforts to build and scale algorithmic selection pipelines, feedback loops, and dataset reliability frameworks across exabytes of data. This includes hundreds of billions of web pages, code repositories, scientific documents, and multimodal assets, with the goal of directly advancing the pre-training, post-training, and multimodal capabilities of Gemini and our future foundation models.

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.

Responsibilities:

• Provide technical leadership and people management for a team of Software Engineers delivering AI-ready training datasets and data quality infrastructure.
• Architect, build, and evolve large-scale distributed data processing pipelines, signal-serving platforms, and dataset observability systems with rigorous reliability and Service Level Objective (SLO) guarantees.
• Partner closely with peer tech lead managers, Über Tech Leads (UTLs), and DeepMind researchers to drive the end-to-end roadmap for pre-training, post-training, and multimodal training data delivery
• Design and scale web-scale algorithms and optimization frameworks including crawl targeting, document selection, deduplication, spam/low-value filtering guardrails, and objective functions operating across hundreds of billions of documents.
• Establish rigorous data quality metrics and automated anomaly detection.