Minimum qualifications:- Bachelor's degree in Computer Science, Management Information Systems, or other technical field, or equivalent practical experience.
- 4 years of manufacturing or validation experience with CPU, dGPU, or TPU.
- 3 years of experience with quality and reliability of technical infrastructure.
- Experience with semiconductor and production manufacturing flows, and implementing test screens, ATE and DFT tests, and transistor parametrics.
- Experience troubleshooting and advocating for customer needs.
- Experience with System Architecture (CPU, TPU, I/O interfaces, or memory).
Preferred qualifications:- Experience working with distributed systems, and familiarity with common solutions, design patterns, or best practices.
- Experience working directly with AI/ML computing hardware, including GPUs or other accelerators.
- Familiarity with containerization and orchestration technologies like Kubernetes or Slurm in an on-prem or cloud environment.
- Experience with systems automation, and systems design and debug.
- Experience working with vendors or customers.
- Advanced understanding of transistor fabrication and ASIC assembly processes.
About the jobOur AI Infrastructure Engineering Support team is dedicated to ensuring our customers get the most out of their Google Cloud hardware investment. As a Customer Quality Engineer (Hardware Engineer), you will be working alongside customers, understanding and improving hardware quality. You will be driving multiple complex technical challenges, understanding customer impacts, and driving internal and external teams to maximize quality. In this role, you will represent the customer, collaborating with engineering and product teams to drive continuous improvement in our products and services.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $159000 - $230000 (USD) 15% bonus target equity benefits
Responsibilities- Track and manage customer hardware observations, helping guide priorities and resolution
- Join with internal engineering teams to understand diagnosis and gauge impact 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. Interact with our Site Reliability Engineering (SRE) teams to drive high-quality attainment.
- Understand the reliability and quality implications of hardware and software modifications and help teams understand how modifications could impact deployed hardware
- Work directly with customers to understand hardware quality, track future quality improvements, and manage future changes that could improve quality on deployed hardware.
