Lead Engineer, Shopping Product Graph, Data Infrastructure

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.

The Shopping Graph is Google's dataset of commercial information powering shopping experiences across Search, Gemini, YouTube, and Ads.

Its key subset, the Shopping Product Graph, models billions of products, their attributes, and their relationships using data from web crawls and merchant or manufacturer catalogs.

Our team builds this model by discovering, creating, and linking entities. We enrich products with attributes, map merchant listings, and define relationships between products, offers, and brands. Using ML/AI for large-scale data clustering and Large Language Models (LLMs) for feature extraction, we ensure high accuracy. This product understanding powers core features across Google, from generating product responses in Search to improving Ads quality and enabling product tagging for YouTube creators.

People shop on Google more than a billion times a day - and the Commerce team is responsible for building the experiences that serve these users. The mission for Google Commerce is to be an essential part of the shopping journey for consumers - from inspiration to to a simple and secure checkout experience - and the best place for retailers/merchants to connect with consumers. We support and partner with the commerce ecosystem, from large retailers to small local merchants, to give them the tools, technology and scale to thrive in today’s digital world.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $262000 - $364000 (USD) + 25% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Set the technical outlook and design the data infrastructure to support the necessary scale, resilience, and rich feature set that clients depend on. Write robust code to solve ambiguous, large-scale problems.
• Enable fast iterations on product graph quality through data-driven decision-making supported by experiments and evaluations.
• Collaborate closely with cross-product area partners to leverage their expertise and infrastructure, translating techniques into tangible, real-world product impact.
• Work with other engineering leads, Product Managers, and executives to identify areas for further improvement in the broader shopping graph, ensuring all relevant domains align seamlessly.
• Guide and mentor executive developers and technical leads, elevating the overall technical competency of the organization.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
  
• 8 years of experience in machine learning architecture.
• Experience with graph storage and knowledge base systems.
• Experience with data pipeline infrastructure and quality.
• Experience leading engineering velocity and experimentation.

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  
• 8 years of experience with data structures and algorithms.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
  
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience with knowledge engineering.