GPU Manufacturing Automation & Debug Engineer

Amazon Web Services (AWS)

Cupertino (CA)

On-site

USD 149,000 - 201,000

Full time

7 days ago
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Benefits offered by this job

Health insurance
401(k) matching
Paid time off
RSUs

Job summary

Amazon Data Services, Inc. in Cupertino is seeking a Manufacturing Systems Development Engineer to own manufacturing test software, tooling, and hardware debug for GPU-based server platforms. You will debug line failures, build diagnostics, and automate tests to improve yield and throughput.

You will work across firmware, kernel, drivers, and PCIe subsystems, traveling ~25% to ODM/CM sites and production ramps to ensure quality at scale.

Qualifications

  • 2+ years of non-internship professional software development.
  • 3+ years of experience debugging hardware systems on manufacturing lines.
  • Experience with Linux boot, kernel, drivers, and OS-level diagnostics.
  • Proficiency in at least one modern language (Python, C/C++, Java).
  • Ability to travel domestically and internationally (~25%).

Responsibilities

  • Debug complex system-level failures across compute, storage, GPU, networking, power and thermal domains.
  • Perform RCCA for yield detractors and test-escapes in manufacturing.
  • Develop and maintain manufacturing test software and diagnostic tools.
  • Build automation to reduce manual triage and improve yield.
  • Collaborate with hardware, firmware and ODM/CM teams on testability and diagnosis.

Skills

Software development
Linux debugging
Hardware debugging
Manufacturing test automation
Programming languages
Travel willingness

Job description

Amazon Data Services, Inc. in Cupertino is seeking a Manufacturing Systems Development Engineer to own manufacturing test software, tooling, and hardware debug for GPU-based server platforms. You will debug line failures, build diagnostics, and automate tests to improve yield and throughput.

You will work across firmware, kernel, drivers, and PCIe subsystems, traveling ~25% to ODM/CM sites and production ramps to ensure quality at scale.

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