Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

Tailor your resume to this AI Infrastructure Global Technical Sales Lead role.

Rezi rewrites your resume against Google's job description. Free.

Resume score gauge reading 58 out of 100

Don't guess if your resume is good enough.

See how it scores against the AI Infrastructure Global Technical Sales Lead posting at Google — free, in seconds.

About the Role

Serve as a field technical leader guiding enterprise AI infrastructure strategy. Bridge the gap between product management, solution engineering, and go-to-market teams to drive strategic customer outcomes. 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. Combine pre-sales systems architecture expertise with clear executive communication to help customers evaluate, design, and scale large-scale AI infrastructure solutions.

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.

Requirements

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.
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.

Skills

HPC
AI networking
GPU/accelerator application performance
Technical sales strategies
Enterprise customers
Distributed organizations
AI/ML compute infrastructure
Data center solutions
Technical stakeholders
Executive leaders
System architecture designs
Workload sizing
Mixed-compute deployments
TPU and GPU clusters
Backend operational layers
Distributed systems
Specialized accelerator hardware
Large model training/inference stacks
Hardware roadmaps
Software tooling
Platform capabilities
Troubleshooting
Benchmarking
Optimizing workloads
Heterogeneous or mixed-compute environments
Distributed training and inference frameworks
Collective communications libraries
NCCL
Modern AI software stacks
C-suite executives

Location

Work Type

Full-time

Experience Level

10 years of experience

Education Level

Bachelor's degree or equivalent practical experience
Master’s degree or PhD in Computer Science, Computer Engineering, or a related field

Salary/Compensations

US: $192000 - $267000 (USD) + 42.86% bonus target + equity + benefits

Benefits

42.86% bonus target
equity
benefits

About the Company

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.
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.

Equal Opportunity

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