Job Description

Minimum qualifications

• Bachelor's degree in a technical field, or equivalent practical experience.
• 8 years of experience in product management with a focus on infrastructure strategy or capacity planning.
• Experience partnering with Finance and Engineering leadership on multi-year infrastructure investment decisions.
• Experience with inference and infrastructure components.
• Experience building forecasting, planning, or resource-allocation system.

Preferred qualifications

• Master's degree or MBA in a technical, operations research, or quantitative field.
• Experience planning accelerator fleets (TPU or GPU), including chip qualification, model-to-chip compatibility, and fungibility constraints.
• Experience with machine learning inference workloads, and with the unit economics that connect tokens, throughput, utilization, and cost.
• Experience translating heterogeneous demand signals into a single allocation model across compute, memory, storage, and network resources.
• Experience with data residency and in-country deployment requirements for regulated markets.

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.

For inference to be successful, capacity planning needs to be a product. Cloud AI serves Gemini and partner frontier models to external developers, enterprise customers, and internal products. These commitments depend on securing the right accelerators, in the right regions, at the right time, and predicting needs over a year in advance. Today, this planning is distributed across product, engineering, Finance, and go-to-market teams.

In this role, you will own the product strategy and planning process end-to-end. You will define how Cloud AI ingests demand and matches it against supply across first- and third-party data centers, and how it allocates constrained capacity when demand exceeds supply. You will also determine how these products should work with our inference stack to improve model economics.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

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

• Own the product strategy and roadmap for compute demand planning, supply planning, and allocation across Cloud AI.
• Establish an 18-24 month forward-looking demand signal segmented by customer, product surface, model family, consumption type, location, and capacity tranche.
• Define and standardize translation functions that convert business units into infrastructure units, ensuring changes in one resource layer propagate to dependent layers.
• Partner with Infrastructure, SRE, and Finance to create a near-real-time view of supply across first-party and third-party data centers.
• Build the allocation and prioritization model governing constrained capacity and drive scenario analysis ahead of model launches, large customer requests, or chip qualification delays.