About the role

What will you do at Google?

MINIMUM QUALIFICATIONS:

* Bachelor's degree in Computer Science, Management Information Systems, or

other technical field, or equivalent practical experience.

* 6 years of experience with technical infrastructure (deployment, maintenance,

and troubleshooting), and quality and reliability of technical

infrastructure.

* 6 years of debug or validation experience with CPU, dGPU, or TPU

* 5 years of experience with hardware debug (silicon debug, platform debug, IO

interface, or memory analysis).

* Experience debugging technical issues across the stack (hardware faults,

low-level software, networking, virtualization, kernel drivers, firmware, or

performance)

* Experience with Linux/Unix systems and debugging issues across the

hardware/software boundary on enterprise-grade server infrastructure.

PREFERRED QUALIFICATIONS:

* Experience working with large-scale distributed systems, and familiarity with

common solutions, design patterns, or best practices.

* Experience working directly with AI/ML computing hardware, including GPUs or

other accelerators.

* Experience with systems automation, and with systems design and debug.

* Experience with ML frameworks (e.g., TensorFlow, Pytorch), and understanding

of the AI/ML training and inference lifecycle.

* Familiarity with containerization and orchestration technologies like

Kubernetes or Slurm in an on-prem or cloud environment.

* Understanding of memory and high-speed IO technologies.

ABOUT THE JOB:

Our AI Infrastructure Engineering Support team is dedicated to ensuring our

customers get the most out of their Google Cloud hardware investment. As a

Platform Application Engineer (Hardware Engineer), you will be focused on

solving customer observations by, driving deep hardware analysis, debug, and

issue resolution through to the root cause. You will dive deep into complex

technical challenges, troubleshoot critical issues across the platform, and

provide resolutions in both short-term and long-term platform solutions. In this

role, you will represent the customer solution, collaborating tightly with

engineering and product teams to drive continuous improvement in our products

and services.Google Cloud accelerates every organization’s ability to digitally

transform its business and industry. We deliver enterprise-grade solutions that

leverage Google’s cutting-edge 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: $188000 - $274000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google

[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:

* Manage customer’s problems through effective diagnosis, resolution, or

implementation of new investigation tools to increase productivity on AI/ML

infrastructure.

* Work closely with multiple Product, Quality, and Engineering teams to improve

the product, and interact with our Site Reliability Engineering (SRE) teams

to understand behaviors.

* Debug platform hardware and silicon-related issues to drive root-cause

resolution and develop permanent improvements.

* Drive understanding of AI/ML workloads and underlying hardware architectures

by troubleshooting, reproducing, determining the cause for customer reported

issues, and building tools for faster diagnosis.

* Act as a consultant and subject matter expert for internal stakeholders in

Engineering and Quality organizations to resolve complex deployment and

operational obstacles in AI infrastructure environments.

Which skills does this role require?

Hardware DebugSilicon DebugLinuxUnixKernel DriversFirmwareNetworkingVirtualizationPerformance AnalysisCloud InfrastructureAI/ML InfrastructureCloud AI InfrastructureGPUTPUIO InterfaceMemory AnalysisSite Reliability EngineeringRoot Cause AnalysisEnterprise-grade ServerGCPMachine Learning

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