Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience in product management or a related technical role.
• Experience with cloud, artificial intelligence (AI), machine learning (ML) or technical infrastructure products.
• Experience with machine learning model serving and associated cost drivers (e.g., utilization, Generative AI Scaled Unit (GSU), tokens, Queries Per Second (QPS) per chip).
• Experience partnering with engineering teams to ship products end-to-end.

Preferred qualifications:

• Master's degree in a technology or business related field.
• Experience with pricing, monetization, quota/capacity policy, and unit economics.
• Experience working with large datasets to inform product decisions
• Experience launching products in a technical or enterprise domain.

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.

In this role, you will own the critical intersection of model performance, infrastructure capacity, and commercial strategy for Vertex AI and Gemini. You will translate how efficiently our models run into how we price, package, and govern access to scarce compute, designing consumption offerings, commitment models, and PayGo/quota policy that balance customer value against physical capacity constraints. Your role demands both technical depth (model serving, GSUs, latency and efficiency profiles) and commercial instinct (pricing, commitments, margin). You will build hard-won consensus across engineering, finance, capacity, and sales, and your decisions will directly shape utilization, margin, and reliability across the entire platform. If you want to productize availability itself at the frontier of AI, this is the seat.A conversational AI tool that enables users to collaborate with generative AI and help augment their imagination, expand their curiosity, and enhance their productivity. 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

• Analyze efficiency across the multi-modal product family (e.g., TPS/QPS per chip, per GSU) and turn it into data-driven capacity and pricing decisions.
• Define consumption tiers and set Provisioned Throughput (PT) burndown rates for new model launches to reflect actual capacity consumed.
• Introduce spend-based and capacity-based commitments to provide customer flexibility while securing predictable, long-term demand.
• Define PayGo policies and set quotas and limits for internal and external customers to prevent capacity crunches and ensure fair resource sharing.
• Partner across engineering, finance, capacity planning, and sales to align product offerings with physical hardware realities.