Field Application Engineer, Cloud AI Infrastructure

Google•Kirkland, WA

13h•$132,000 - $189,000•Onsite

About The Position

The Field Application Engineer (Hardware Engineer) is an on-site, external-facing trusted advisor to Google Cloud customers, helping them get the most out of their cloud hardware investment. The role focuses on hardware analysis, debugging, and resolving issues across the platform, including complex technical problems, with expert solutions that help customers innovate with confidence. The engineer represents the customer and collaborates with engineering, product, quality, and Site Reliability Engineering teams to improve Google Cloud products and services. The role includes developing a deep understanding of AI/ML workloads and underlying hardware architectures, and supporting customers through deployment and operational challenges in AI infrastructure environments.

Requirements

Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
At least 2 years of debug or validation experience with CPU, dGPU, or TPU.
At least 2 years of experience with technical infrastructure deployment or maintenance, troubleshooting, and infrastructure quality and reliability.
At least 2 years of experience with hardware debug involving silicon, platform, IO interface, or memory analysis.
Experience with Linux/Unix systems and debugging issues across the hardware/software boundary on enterprise-grade server infrastructure.
Experience troubleshooting and triaging technical issues across the stack, such as hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance.

Nice To Haves

Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
Experience with systems automation, systems design, and debugging.
Experience working with vendors or customers.
Experience with distributed systems and familiarity with common solutions, design patterns, or best practices.
Experience with ML frameworks such as TensorFlow or PyTorch, and understanding of the AI/ML training and inference lifecycle.
Advanced understanding of memory and high-speed IO technologies.

Responsibilities

Participate in on-call activities and manage server and data center CPU- and TPU-based activities, working with primary responders to resolve customer system observations.
Manage customer problems through effective diagnosis, resolution, or implementation of investigation tools to improve productivity on AI/ML infrastructure.
Work closely with Product, Quality, and Engineering teams to improve the product, and interact with Site Reliability Engineering teams to drive high-quality attainment.
Troubleshoot and reproduce customer-reported issues, determine root causes, and build tools for faster diagnosis while developing an in-depth understanding of AI/ML workloads and underlying hardware architectures.
Act as a consultant and subject matter expert for Engineering, Sales, and customer stakeholders to resolve complex deployment and operational obstacles in AI infrastructure environments.
Research complex technical issues, troubleshoot critical issues across the platform, and provide expert solutions.