Platform Application Engineer, Cloud AI Infrastructure

Google LLC; Austin, Kirkland (TX, WA); On-site; USD 159,000 - 230,000; Full time

Benefits offered by this job

Health, dental, vision, life, and disa

401(k) with company match

Paid Time Off: 20 days per year

Sick Time: 40 hours/year (increased to

Maternity Leave: 28-30 weeks

Baby Bonding Leave: 18 weeks

Holidays: 13 paid days per year

Job summary

Google LLC in Austin, TX is seeking a Share Platform Application Engineer for Cloud AI Infrastructure. You will dive deep into hardware analysis, debug platform issues, and collaborate with engineering and product teams to drive improvements across Google Cloud hardware investments.

The role requires a Bachelor's in CS/MIS or equivalent, 4 years hardware/infra debugging experience, and Linux/Unix proficiency. Familiarity with AI hardware, GPUs, and containers (Kubernetes) is a plus.

Qualifications

• Bachelor's degree in Computer Science, Management Information Systems, or other technical field, or equivalent practical experience.
• 4 years of debug or validation experience with CPU, dGPU, or TPU.
• 4 years of experience with technical infrastructure (deployment, maintenance, and troubleshooting), and quality and reliability of technical infrastructure.
• 3 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.

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 behaviours.
• 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.

Skills

Debugging

Infrastructure troubleshooting

Linux/Unix

Education

Bachelor's degree in Computer Science, MIS, or other technical field

Tools

Kubernetes

Slurm

TensorFlow

PyTorch

Job description

Share Platform Application Engineer, Cloud AI Infrastructure

corporate_fare Google place Austin, TX, USA ; Kirkland, WA, USA

info_outline X 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: Austin, TX, USA; Kirkland, WA, USA.

• Bachelor's degree in Computer Science, Management Information Systems, or other technical field, or equivalent practical experience.
• 4 years of debug or validation experience with CPU, dGPU, or TPU.
• 4 years of experience with technical infrastructure (deployment, maintenance, and troubleshooting), and quality and reliability of technical infrastructure.
• 3 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 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.
• Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
• 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.
• Advanced understanding of memory and high-speed input/output (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 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: $159000 - $230000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google .

• 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 behaviours.
• 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.

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See also Google's EEO Policy, Know your rights: workplace discrimination is illegal, Belonging at Google, and How we hire .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

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Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.