Senior Product Manager, Images Feed Quality, Google Search

About the job

Join us to build the future of the Google Images experience. Building on the foundation of Google Images, one of the world's largest visual discovery surfaces, we are on a mission to transform how billions of people explore, evaluate, and act on visual information. We are evolving Google Images from a transactional, query-based retrieval grid into a fluid, personalized, and AI-powered visual guide.

At the heart of this transformation is building the next-generation inspiration feed. More than just answering queries, we are reimagining how users around the world browse, discover trends, and explore their passions across fashion, home, beauty, travel, and culture. This is a strategic priority for Google Search, offering a rare opportunity to reimagine a core consumer product for the next generation of visual-first users.

As the Product Manager for Images Feed Quality, you will own the core intelligence engine behind the newly launched Images Feed. Being at the intersection of consumer product intuition and frontier AI research, you will define how we model human taste, retrieve and rank multimodal content, and dynamically tune the feed in real time.

You will partner closely with your cross-functional team and Google DeepMind (GDM) to bring foundation models and generative retrieval to users at global scale, creating a feed that feels deeply personal, aesthetically inspiring, and effortlessly controllable. This is a technical role requiring deep expertise across the AI stack, bringing together world knowledge with personal context to deliver a highly inspiring user experience.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.

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

US: $192000 - $278000 (USD) + 20% bonus target + equity + benefits

Learn more about

benefits at Google.

Responsibilities

• Lead the ranking, retrieval, and personalization roadmap for the Images Feed, balancing deep user familiarity with serendipitous discovery of new topics.
• Drive visual feed relevance, appeal, freshness, and persistence, managing the trade-offs between AI quality and load latency.
• Architect the intelligence loop connecting Search, Saves, and the Images Feed, leveraging unique cross-product signals.
• Collaborate with research and engineering to productionize frontier AI models, including multimodal embeddings, generative retrieval, and learned recommendation stacks.
• Build evaluation pipelines and LLM autoraters measuring visual discovery quality, topline engagement key performance indicators (KPIs), and experimentation methodology.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or a related technical role.
• 3 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc.).
• Experience in software engineering for recommendation systems or feed ranking architectures.

Preferred qualifications:

• Master's degree in a technology or business related field.
• 5 years of experience working cross-functionally with engineering, UX/UI, sales finance, and other stakeholders.
• 5 years of experience in a business function or role (e.g., strategic marketing, business operations, consulting).
• 4 years of experience in a role preparing and delivering technical presentations to executive leadership.
• Experience translating advanced machine learning research into highly stable, consumer-facing feeds and recommendation products at global scale.
• Deep technical understanding of retrieval, ranking techniques, value modeling, and the nuances of recommendation systems.