Product Manager, Multimodal Data Pipelines, AI Foundations

Job description

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 backend infrastructure (e.g., for search or AI applications).

Preferred qualifications

Master's degree in a technology or business related field. 2 years of experience in software development or engineering. 1 year of experience in technical leadership. Experience with data processing using Large Language Models (LLMs), Model Development, Model Training, Retrieval-Augmented Generation (RAG). Exceptional cross-functional leadership skills, with the ability to influence without authority across engineering, research, and product areas.

Excellent problem-solving skills, with the ability to define user journeys and success metrics. 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.

This new AI development phase demands radically innovating how we acquire, process, and index fresh, high-quality multimodal data. Raw, web-scale crawled data must become trusted, structured data to fuel Large Language Model (LLM) training and enable agentic applications to retrieve and reason over context.