Field Application Engineer Manager, Cloud AI Infrastructure

Google•Kirkland, WA

14h•$236,000 - $329,000

About The Position

Google Cloud accelerates organizations’ ability to digitally transform with its infrastructure, platform, industry solutions, and expertise. Its AI Infrastructure Engineering Support team helps customers get the most out of their Google Cloud hardware investment. The Field Application Engineer Manager (Hardware Engineer) leads a team serving as on-site, external-facing trusted advisors to customers, supporting AI/ML infrastructure environments and resolving complex technical and operational issues.

Requirements

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 issues across the hardware/software boundary on enterprise-grade server infrastructure.
5 years of experience in technical leadership.
3 years of experience with technical infrastructure deployment, maintenance, and troubleshooting, and with its quality and reliability.
3 years of debug or validation experience with CPU, GPU, or TPU.
Experience troubleshooting and triaging technical issues across the stack, such as hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance.

Nice To Haves

Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
Experience with systems automation, systems design, and debugging.
Experience with ML frameworks such as TensorFlow or PyTorch, and understanding of the AI/ML training and inference life cycle.
Experience with distributed systems and familiarity with common solutions, design patterns, or best practices.
Familiarity with containerization and orchestration technologies such as Kubernetes or Slurm in an on-premises or cloud environment.
Understanding of memory and high-speed I/O technologies.

Responsibilities

Lead a team participating in on-call activities and working with primary responders to resolve system observations; provide leadership, mentorship, and career development.
Manage customer problems through diagnosis, resolution, or implementation of investigation tools that improve productivity on AI/ML infrastructure.
Work with Product, Quality, and Engineering teams to improve the product, and collaborate with Site Reliability Engineering (SRE) teams to drive high-quality attainment.
Build in-depth understanding of AI/ML workloads and hardware architectures by troubleshooting and reproducing customer-reported issues, determining root causes, and creating tools for faster diagnosis.
Serve as a consultant and subject matter expert for Engineering, Sales, and customer stakeholders to resolve complex deployment and operational obstacles in AI infrastructure environments.