Platform Application Engineer, Cloud AI Infrastructure

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

Benefits offered by this job

Health insurance

Dental insurance

Vision insurance

Life insurance

Disability insurance

401(k) with company match

PTO 20 days per year

Sick time 40 hours/year

Maternity leave 28-30 weeks

Baby bonding leave 18 weeks

Holidays 13 days

Job summary

Google is seeking a Platform Application Engineer (Hardware Engineer) to solve complex hardware observations, perform deep hardware analysis and debugging, and drive root-cause resolutions across AI/ML infrastructure. You will work with engineering and product teams to continually improve our Google Cloud hardware solutions.

We leverage cutting-edge technology to empower customers worldwide, with emphasis on scalable, reliable platform performance and collaboration across SRE and development

Qualifications

• Bachelor's degree in Computer Science, MIS, or related field, or equivalent practical experience.
• 4 years of debug or validation experience with CPU, dGPU, or TPU.
• 4 years in technical infrastructure deployment, maintenance, and troubleshooting.
• 3 years hardware debug (silicon, platform, IO, or memory analysis).
• Experience debugging issues across the stack (hardware faults, software, networking, kernel drivers, firmware).
• Experience with Linux/Unix systems and debugging across hardware/software boundary on enterprise servers.

Responsibilities

• Diagnose and resolve AI/ML infrastructure issues to improve platform reliability.
• Collaborate with Product, Quality, and Engineering teams; engage SRE for behavior understanding.
• Debug platform hardware and silicon issues to drive root-cause resolution.
• Develop tools to diagnose AI/ML workloads and underlying hardware architectures.
• Act as SME for internal stakeholders to resolve deployment and operational obstacles.

Skills

CPU debugging

GPU/TPU debugging

Infrastructure debugging

Hardware debugging

Linux/Unix

Education

Bachelor's degree in Computer Science or related field

Tools

Kubernetes

Slurm

Job description

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.

Minimum 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.

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

Responsibilities

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 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 .