Field Application Engineer Manager, Cloud AI Infrastructure
Austin, TX, USA +1 · Manager · Full-time · Posted Oct 27, 2026
- Location
- Austin, TX, USA
Kirkland, WA, USA - Experience
- 8 years with Linux/Unix systems and hardware/software debugging; 5 years in technical leadership; 3 years in technical infrastructure; 3 years of CPU, GPU, or TPU debug or validation.
- Education
- Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or an IT-related field, or equivalent practical experience.
About the job
Manages a team of field application engineers who advise customers on Google Cloud AI infrastructure and troubleshoot hardware and software issues.
What you'll do
- Lead, mentor, and develop the team, including coordinating on-call work to resolve system observations.
- Diagnose customer problems, identify root causes, and build investigation tools to speed troubleshooting.
- Partner with Product, Quality, Engineering, SRE, Sales, and customers to improve product quality and resolve AI infrastructure deployment and operational challenges.
What they ask for
- Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or an IT-related field, or equivalent practical experience.
- 8 years of experience with Linux/Unix systems and debugging across the hardware/software boundary on enterprise-grade server infrastructure.
- 5 years of experience in technical leadership.
- 3 years of technical infrastructure experience, 3 years of CPU, GPU, or TPU debug or validation experience, and experience troubleshooting technical issues across the stack.
Nice to have
- Experience with AI/ML computing hardware, ML frameworks, and the AI/ML training and inference life cycle.
- Experience with systems automation and design, distributed systems, Kubernetes or Slurm, or memory and high-speed IO technologies.
Pay
USD $236000 - $329000 base salary
Tools
Linux/Unix, CPU, GPU, TPU, TensorFlow, PyTorch, Kubernetes, Slurm
AI focus
Supports customer AI/ML infrastructure by troubleshooting hardware and workloads, identifying root causes, and developing faster diagnostic tools.
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