Senior Product Manager, AI Models and Agentic Platform, Google Distributed Cloud

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or 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 integrating generative AI tools or LLM interfaces into workflows.

Preferred qualifications:

• Master's degree in a technology or business related field.
• Experience with Large Language Model (LLM) serving architectures, inference optimization frameworks (e.g., vLLM, SGLang, TensorRT-LLM), and hardware accelerators (GPUs, TPUs).
• Experience building or managing products for hybrid cloud, sovereign cloud, air-gapped data centers, or compliance-restricted industries (e.g., public sector, defense, financial services).
• Understanding of model evaluation methodologies, safety and governance frameworks, and hardware/software performance profiling.
• Excellent executive presence and customer-facing communication skills, with the ability to navigate ambiguous cross-functional landscapes and align complex technical priorities.

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.

Google Distributed Cloud extends Google Cloud’s infrastructure, services, and advanced AI capabilities directly into customer-operated data centers, edge locations, and sovereign, highly regulated environments.

As the Senior Product Manager for Gemini on Google Distributed Cloud, you will be the lead product owner responsible for the end-to-end model serving life-cycle, platform runtime, and roadmap execution for Gemini foundation models across both connected and air-gapped (fully disconnected) environments.

In this role, you will define the multi-quarter strategy to bring Google’s frontier models to on-premises hardware architectures and accelerator platforms. You will collaborate closely with AI research, foundation model development, and cloud platform teams across Google, as well as strategic enterprise customers and sovereign government institutions, ensuring that local AI deployments deliver cloud-grade fidelity, uncompromising safety, and enterprise reliability.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s 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 delivery schedule for Gemini models on Google Distributed Cloud across both connected and air-gapped environments, ensuring predictable release cadences and capability between public cloud and on-premises deployments.
• Partner with systems and runtime engineering teams to optimize critical inference metrics, such as Time to First Token, Time Per Output Token, and aggregate throughput, while integrating advanced capabilities into the local serving stack.
• Serve as the primary product interface to AI research, model development, and safety teams across Google; establish shared release criteria, define model life-cycle and support policies, and oversee the integration of on-premises safety mechanisms, including intellectual property protections and model guardrails.
• Define and streamline end-to-end experiences for developers and system operators—spanning developer APIs, software development kits command-line tooling, identity and access management, local telemetry, and hardware capacity planning.