Senior Platform Application Engineer, Cloud AI Infrastructure

Google; Sunnyvale (CA); On-site; USD 188,000 - 274,000; Full time

Benefits offered by this job

Health insurance

401(k) with company match

Paid time off (vacation) 20 days/year

Sick time 40 hours/year

Maternity leave 28-30 weeks

Baby bonding leave 18 weeks

13 paid holidays per year

Job summary

Google is hiring for an AI Infrastructure Platform Application Engineer (Hardware Engineer) to diagnose and resolve complex hardware observations, drive deep analysis and root-cause fixes across AI/ML infrastructure.

You will partner with engineering and product teams to continuously improve hardware platforms and software for customers using Google Cloud. The role requires extensive experience with server hardware, Linux, and debugging across the stack.

Qualifications

• Bachelor's degree or equivalent practical experience in a technical field.
• 6 years of experience with technical infrastructure deployment, maintenance, and troubleshooting.
• 6 years of debug/validation experience with CPU, dGPU, or TPU.
• 5 years of hardware debugging (silicon/platform IO/interface/memory).
• Experience debugging across hardware/software stack (hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance).
• Experience with Linux/Unix systems and debugging across hardware/software boundaries on enterprise-grade server infrastructure.

Responsibilities

• Manage customer problems through effective diagnosis, resolution, or new tool development to improve AI/ML infrastructure productivity.
• Collaborate with Product, Quality, and Engineering teams to improve products and support Site Reliability Engineering teams.
• Debug platform hardware and silicon-related issues to drive root-cause resolution and permanent improvements.
• Understand AI/ML workloads and hardware architectures; troubleshoot, reproduce, identify causes, and build faster diagnosis tools.
• Act as a consultant for internal stakeholders to resolve deployment and operational challenges in AI infrastructure environments.

Skills

Platform debugging

Hardware debugging

AI/ML hardware

Linux/Unix

Cross-stack debugging

Education

Bachelor's degree in Computer Science or related field

Tools

Linux/Unix systems

CPU/dGPU/TPU hardware

Kubernetes/Slurm familiarity

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 (statutory, where applicable); 5 days/event (discretionary)
• 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, 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

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 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.

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 .