Customer Quality Engineer, Cloud AI Infrastructure

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.
• - 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.

Minimum qualifications:

Preferred qualifications:

Qualifications

• 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.