Job Description

Minimum qualifications

• Bachelor's degree or equivalent practical experience.
• 10 years of experience in technical pre-sales, systems engineering, or technical consulting focused on modern HPC, AI networking, or GPU/accelerator application performance.
• Experience building and executing technical sales strategies for enterprise customers across distributed organizations.
• Experience architecting and selling full-stack AI/ML compute infrastructure and data center solutions.
• Experience engaging with, and presenting to, technical stakeholders and executive leaders.

Preferred qualifications

• Master’s degree or PhD in Computer Science, Computer Engineering, or a related field.
• Experience partnering with ecosystem players, including OEMs, ODMs, and specialized cloud providers.
• Demonstrated experience troubleshooting, benchmarking, and optimizing workloads running across heterogeneous or mixed-compute environments.
• Deep knowledge of distributed training and inference frameworks, collective communications libraries (e.g., NCCL), and modern AI software stacks.
• Track record of building alignment with C-suite executives and translating complex distributed systems concepts into strategic business outcomes.

About the job

When leading companies choose Google Cloud, it's a huge win for spreading the power of cloud computing globally. Once educational institutions, government agencies, and other businesses sign on to use Google Cloud products, you come in to facilitate making their work more productive, mobile, and collaborative. You listen and deliver what is most helpful for the customer. You assist fellow sales Googlers by problem-solving key technical issues for our customers. You liaise with the product marketing management and engineering teams to stay on top of industry trends and devise enhancements to Google Cloud products.

Google Cloud accelerates organizations’ ability to digitally transform their business with the best infrastructure, platform, industry solutions, and expertise. We deliver enterprise-grade solutions that leverage Google’s technology to help our customers solve their most critical business problems.

As an AI Infrastructure Global Technical Sales Lead, you will serve as a field technical leader guiding our enterprise AI infrastructure strategy. In this high-impact role, you will bridge the gap between product management, solution engineering, and go-to-market teams to drive strategic customer outcomes.

You will act as the technical lead for complex customer engagements, helping technical buyers and business executives navigate modern data center strategies, operation layers, and workload scoping. By combining pre-sales systems architecture expertise with clear executive communication, you will help customers evaluate, design, and scale large-scale AI infrastructure solutions.

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 team 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 - $267000 (USD) + 42.86% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Lead customer-facing technical engagements, partnering with C-level executives, research leads, and engineering teams to define AI infrastructure strategy and optimize price-per-flop performance.
• Guide customers through complex system architecture designs, including workload sizing, mixed-compute deployments, and performance trade-offs between TPU and GPU clusters.
• Partner cross-functionally with Core Infrastructure Engineering, Specialist Sales, and Product Management to resolve technical blockers on tier-one engagements and develop global sales playbooks.
• Define requirements across backend operational layers and distributed systems to accelerate adoption of specialized accelerator hardware and large model training/inference stacks.
• Represent customer requirements back to product and engineering teams to influence future hardware roadmaps, software tooling, and platform capabilities.