Our AI Infrastructure Engineering Support team is dedicated to ensuring our customers get the most out of their Google Cloud hardware investment.
As a Field Application Engineer (Hardware Engineer), you will be an on-site, external-facing trusted advisor to customers, driving hardware analysis, debug, and issue resolution. You will do in-depth research into complex technical issues, troubleshoot critical issues across the platform, and provide expert solutions that help customers innovate with confidence.In this role, you will represent the customer, collaborating 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: $132000 - $189000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Participate in on-call activities and manage server and data center CPU- and TPU-based activities, working with primary responders to resolve customer system observations.
- Manage customers' problems through effective diagnosis, resolution, or implementation of new investigation tools to increase productivity on AI/ML infrastructure.
- Work closely with Product, Quality, and Engineering teams to improve the product. Interact with our Site Reliability Engineering (SRE) teams to drive high-quality attainment.
- Develop an in-depth understanding of AI/ML workloads and underlying hardware architectures by troubleshooting, reproducing, determining the root cause for customer-reported issues, and building tools for faster diagnosis.
- Act as a consultant and subject matter expert for internal stakeholders in Engineering, Sales, and customer organizations to resolve complex deployment and operational obstacles in AI infrastructure environments.
Minimum qualifications:
- Bachelor's degree in Computer Science, Management Information Systems, a related technical field, or equivalent practical experience.
- 2 years of debug or validation experience with CPU, dGPU, or TPU.
- 2 years of experience with technical infrastructure (deployment or maintenance, and troubleshooting), and quality and reliability of technical infrastructure.
- 2 years of experience with hardware debug (silicon, platform, IO interface, or memory analysis).
- Experience with Linux/Unix systems and debugging issues across hardware/software boundary on enterprise-grade server infrastructure.
- Experience troubleshooting and triaging technical issues across the stack (e.g., hardware faults, low-level software, networking, virtualization, kernel drivers, firmware, or performance).
Preferred qualifications:
- Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
- Experience with systems automation, and with systems design and debug.
- Experience working with vendors or customers.
- Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices.
- Experience with ML frameworks (e.g., TensorFlow, PyTorch), and understanding of the AI/ML training and inference lifecycle.
- Advanced understanding of memory and high-speed IO technologies.