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X Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: San José, CA, USA; San Francisco, CA, USA.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in product management or a related technical role.
• 2 years of experience taking technical products from conception to launch (e.g., ideation to execution, end-to-end, 0 to 1, etc).
• Experience integrating generative AI tools or LLM interfaces into workflows.
• Experience building and scaling developer platforms, developer-facing APIs/SDKs, or general back-end infrastructure (e.g. for search or AI applications).

Preferred qualifications:

• Master's degree in a technology or a business related field.
• 2 years of experience in software development or engineering.
• Experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and vector search.
• Ability to define user journeys and success metrics along with excellent analytical skills.
• Exceptional cross-functional leadership skills with the ability to influence without authority across engineering, research, and product areas.

About the job

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.

Large Language Models (LLMs) need access to fresh, specialized data to give users helpful responses. Doing this at scale while keeping things flexible enough to let developers try new ideas quickly involves bridging the gaps between many infrastructure capabilities: chunking, inference, embeddings retrieval, and more. You will work with stakeholders across the Context and Understanding organization to make sure Google's AI powered experiences can leverage the Retrieval-Augmented Generation (RAG) setups to optimize cost, quality, and latency.

Behind the scenes, RAG systems rely on vector (embeddings) search as a critical technology to enable fast, cheap, semantic queries. You will also work with the broader AI Foundations organization to guide the evolution of our embeddings capabilities across a number of storage and serving systems.

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

US: $163000 - $236000 (USD) 15% bonus target equity benefits

Learn more about benefits at Google .

Responsibilities

• Partner with research teams across DeepMind, Research, and Core to advance the next-generation RAG technologies at Google, and make them available across the company.
• Partner with clients to unlock new business opportunities by resolving critical bottlenecks in Google's RAG technologies.
• Drive the horizontal product outlook and road map to ensure smooth and efficient journeys across multiple products in the AI Foundations portfolio.
• Conduct client and external research to surface top client pain points and emerging opportunities.
• Define and track success metrics for user value and developer velocity.