Cloud AI Infrastructure Platform Engineer

Google LLC

Austin, Kirkland (TX, WA)

On-site

USD 159,000 - 230,000

Full time

14 days+

A complete application in a minute

Tailored resume and cover letter, ready to send.

Get past ATS filters

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.