Field Application Engineer, Cloud AI Infrastructure
Kirkland, WA, USA +1 · Full-time · Posted Sep 28, 2026
- Location
- Kirkland, WA, USA
Sunnyvale, CA, USA - Experience
- 4 years in CPU, dGPU, or TPU debug or validation; 3 years in technical infrastructure; 3 years in hardware debug.
- Education
- Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
About the job
Provides on-site customer support for Google Cloud AI infrastructure by diagnosing hardware and system issues, resolving deployment obstacles, and driving product improvements with engineering teams.
What you'll do
- Manage CPU- and TPU-based customer issues, including on-call response, diagnosis, and resolution.
- Troubleshoot and reproduce AI/ML infrastructure problems, identify root causes, and build tools for faster diagnosis.
- Advise customers and internal teams, and collaborate with Product, Quality, Engineering, and SRE to improve products and reliability.
What they ask for
- 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 its quality and reliability; 3 years of hardware debug experience.
- Experience with Linux/Unix systems and debugging across the hardware/software boundary on enterprise-grade server infrastructure; experience troubleshooting technical issues across the stack.
Nice to have
- Experience with AI/ML computing hardware, systems automation/design/debug, vendors or customers, or distributed systems.
- Experience with ML frameworks (e.g., TensorFlow, PyTorch), AI/ML training and inference, or memory and high-speed IO technologies.
Pay
$159000 - $230000 (USD) base salary
Tools
CPU, dGPU, TPU, Linux/Unix, TensorFlow, PyTorch
AI focus
Troubleshoots and supports AI/ML infrastructure hardware and workloads, including AI/ML training and inference systems.
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