About the job
As a part of the team powering Google Images, the world's largest image search engine, you will be responsible for the ranking of image results and work on all aspects of quality including mining and developing new foundational signals, defining evaluation methods for algorithmic changes, and continuously exploring the latest developments in research to apply them to large-scale problems. In this role, you will help lead technical initiatives across critical new investment areas, including Shopping in Images, Personalization in Image Results and Features, Search Refinements, and the transition toward an AI-Organized Search Results Page.
Responsibilities
Build, mentor, and grow a team of software engineers, guiding their technical direction, career development, and execution.
Drive the technical strategy for evaluating opportunities to improve the current ranking of image results.
Oversee the development of ranking improvements, including advances in retrieval, scoring, and diversification of results.
Guide the team in using a broad spectrum of available technology at Google to solve problems at scale: from Large Language Model (LLMs) like Gemini to ScaM-based retrieval, to more traditional information retrieval techniques.
Reiterate on evaluation methods used for ranking changes, constantly aligning them with evolving product goals and our 2026 strategic investments.
Qualifications
Minimum
Bachelor’s degree, or equivalent practical experience.
8 years of experience in software development and working with algorithms.
2 years of experience in a people management or team leadership role.
Experience coding in C++ or Python.
Experience with data analysis.
Preferred
Master's degree or PhD in Computer Science or related technical field.
Experience managing a team of software engineers.
Experience in Search Quality and ranking systems.
Experience with applied machine learning and ML training and serving at Google.
Experience architecting and deploying machine learning solutions within AI/ML.
Ability to understand product needs, articulate them into tasks for human raters or AI prompts, understand user behavior, analyze data, and interpret model outputs.