Field Application Engineer, Cloud AI Infrastructure

Google; Sunnyvale (CA); Presencial; USD 138.000 - 230.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

401(k) with company match

PTO 20 days

Sick time 40 hours/year (69 hours/year

Maternity leave 28–30 weeks

Baby bonding leave 18 weeks

13 paid holidays

Descripción de la vacante

Google is seeking a Field Application Engineer (Hardware Engineer) to support Google Cloud hardware on-site, acting as a trusted advisor to customers and driving hardware analysis, debugging, and issue resolution.

You will collaborate with engineering and product teams to troubleshoot complex issues, research root causes, and develop tools that speed up diagnostics, while engaging with SRE and sales to ensure effective deployment and operation.

Formación

• Bachelor's degree in Computer Science, MIS, a related technical field, or equivalent practical experience.
• 4 years of experience in debug or validation with CPU, dGPU, or TPU.
• 3 years of experience with technical infrastructure (deployment or maintenance, and troubleshooting), and with quality and reliability of technical infrastructure.
• 3 years of experience with hardware debug (e.g., silicon, platform, IO interface, or memory analysis).
• Experience with Linux/Unix systems and debugging issues across hardware/software boundary on enterprise-grade server infrastructure.
• Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, or virtualization).

Responsabilidades

• Participate in on-call activities for CPU- and TPU-based server work, coordinating with responders to resolve observations.
• Diagnose and resolve customer problems, or create tools to speed AI/ML infra troubleshooting and productivity.
• Collaborate with Product, Quality, and Engineering teams and Site Reliability Engineering to improve the product and services.
• Develop an in-depth understanding of AI/ML workloads and hardware architectures; build tools for faster diagnosis.
• Advise internal stakeholders to resolve deployment and operational obstacles in AI infrastructure environments.

Conocimientos

CPU debugging

dGPU debugging

TPU debugging

System infrastructure

Hardware debugging

Linux/Unix

Troubleshooting across stack

Educación

Bachelor's degree in Computer Science, MIS, or related field

Descripción del empleo

In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:

• Health, dental, vision, life, disability insurance


• Retirement Benefits: 401(k) with company match


• Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment


• Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance


• Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks


• Baby Bonding Leave: 18 weeks


• Holidays: 13 paid days per year

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Kirkland, WA, USA; Sunnyvale, CA, USA.

Minimum qualifications

• Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.


• 4 years of experience in debug or validation with CPU, dGPU, or TPU.


• 3 years of experience with technical infrastructure (deployment or maintenance, and troubleshooting), and with quality and reliability of technical infrastructure.


• 3 years of experience with hardware debug (e.g., silicon, platform, IO interface, or memory analysis).


• Experience with Linux/Unix systems and debugging issues across hardware/software boundary on enterprise-grade server infrastructure.


• Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, or virtualization).

Preferred qualifications

• Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.


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


• Experience working with vendors or customers.


• Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices.


• Experience with ML frameworks (e.g., TensorFlow, PyTorch), and understanding of the AI/ML training and inference lifecycle.


• Advanced 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 Field Application Engineer (Hardware Engineer), you will be an on-site, external-facing trusted advisor to customers, driving hardware analysis, debug, and issue resolution. You will do in-depth research into complex technical issues, troubleshoot critical issues across the platform, and provide expert solutions that help customers innovate with confidence. In this role, you will represent the customer, collaborating 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: $159000 - $230000 (USD) + 15% bonus target + equity + benefits

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 customers' problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure.


• Work closely with Product, Quality, and Engineering teams to improve the product. Interact with our Site Reliability Engineering (SRE) teams to drive high-quality attainment.


• Develop an in-depth understanding of AI/ML workloads and underlying hardware architectures by troubleshooting, reproducing, determining the root cause for customer-reported issues, and building tools for faster diagnosis.


• Act as a consultant and subject matter expert for internal stakeholders in Engineering, Sales, and customer organizations to resolve complex deployment and operational obstacles in AI infrastructure environments.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.