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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 senior 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 large-scale recommendation systems.
About the job:
At Google, we put our users first. The world is always changing, so we need Product Managers who are continuously adapting and excited to work on products that affect millions of people every day.In this role, you will work cross-functionally to guide products from conception to launch by connecting the technical and business worlds. You can break down complex problems into steps that drive product development.
One of the many reasons Google consistently brings innovative, world-changing products to market is because of the collaborative work we do in Product Management. Our team works closely with creative engineers, designers, marketers, etc. to help design and develop technologies that improve access to the world's information. We're responsible for guiding products throughout the execution cycle, focusing specifically on analyzing, positioning, packaging, promoting, and tailoring our solutions to our users.
Building on the foundation of Google Images, the world's third-largest search engine, we see a tremendous opportunity to evolve beyond traditional search to serve the next generation of users. More than just answering questions, we are embarking on a mission to help users explore through visual discovery.
In this technical role, you will require deep expertise across the AI stack, focusing on driving the quality and personalization of the Images Feed. You will bridge the gap between frontier AI research and production at scale, ensuring that the feed seamlessly brings together world knowledge with personal context to deliver a highly inspiring user experience. Your work will provide the essential grounding, retrieval strategies, and evaluation frameworks required to power the next-generation of visual inspiration experiences for millions of users.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:
- Oversee secure, privacy-compliant systems that dynamically surface relevant user data to ground and personalize AI responses.
- Lead the "Quality Hillclimbing" roadmap, balancing high-quality personalization metrics against feed load latency to ensure optimal speed.
- Develop context-specific autoraters and evaluation frameworks to continuously measure AI accuracy, helpfulness, and performance.
- Define intelligence strategies that leverage search interactions to build a continuously evolving, consistent understanding of user preferences.
- Collaborate on technical strategies utilizing state-of-the-art modeling techniques (e.g., Gemembed, Generative Retrieval) to advance the recommendation stack and feed growth.
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