Senior Product Manager, AI and Compute Infrastructure

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in product management or related technical role.
• 2 years of experience working with Infrastructure as a Service (IaaS) and Kubernetes.
• 2 years of experience in infrastructure platforms, microservices, or agentic architectures.
• Experience in the development or launch of products or technologies within artificial intelligence or machine learning (AI/ML).

Preferred qualifications:

• Master's degree in a technology or business related field.
• 4 years of experience in a role preparing and delivering technical presentations to executive leadership.
• 3 years of experience in software development or engineering.
• Experience with AI/ML in security.

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.

ACI Infrastructure delivers the secure, reliable, and unified compute infrastructure powering Google Cloud’s scale. As the foundational glue between hardware NPIs, orchestration, and workloads, we are accountable for the non-negotiable bedrock of platform security and reliability.

The opportunity ahead puts us in the middle of the most impactful work for Google Cloud.

Our mission is to deliver the world's most complete and powerful AI substrate into an uneventful, predictable, and infinitely scalable utility for the Agentic era. This high-stakes engineering represents a unique chance to decouple revenue from hardware constraints.

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

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

US: $192000 - $279000 (USD) + 20% bonus target + equity + benefits

Learn more about [benefits at Google](https://www.google.com/about/careers/applications/benefits/).

Responsibilities

• Evolve the GKE Node OS roadmap (COS, Ubuntu, Windows), aligning release cadences with Kubernetes and optimizing for security and reduced downstream toil.
• Drive the customization strategy, including GKE Image Builder, balancing customer demands for flexibility (BYONI, non-mainstream OSs, custom kernels) with platform supportability.
• Oversee predictable upgrade-in-place strategies to eliminate customer-experienced downtime during capacity constraints.
• Guide the transition and technical enablement for new CPU NPIs across GKE Standard and Autopilot, including Day 0 support.
• Partner with engineering to deliver runtime primitives for GPU/TPU workloads, including containerd strategic alignment, Pod Snapshotting, and image streaming.