Hardware Qualification Engineer, ML Products, Google Cloud

Google Google · Big Tech · Sunnyvale, CA +1

This role focuses on the hardware qualification of machine learning products within Google's data centers. The engineer will create test plans, perform electrical, functional, performance, and reliability testing, analyze results, and drive corrective actions. The role involves using lab equipment and understanding hardware interfaces to ensure quality and reliability of ML hardware infrastructure.

What you'd actually do

  1. Create test plans and coordinate resources across test environments.
  2. Perform electrical, functional, performance and reliability testing for Google's machine learning trays and solutions. Analyze results to ensure that they meet Google's requirements.
  3. Report bugs and drive corrective action where needed with external suppliers and internal SQEs, commodity teams and developers.
  4. Use scopes, protocol analyzers, BERTs and other lab equipment to collect high precision data.
  5. Organize results, communicate findings and incorporate learnings to maintain and improve the qualification process.

Skills

Required

  • Bachelor’s degree in Electrical Engineering, Computer Engineering, a related field, or equivalent practical experience.
  • 2 years of experience working in a board and systems technical environment.
  • Experience with testing requirements, best practices, and algorithms for hyperscale hardware or systems, and using scripting and automation to execute test algorithms and analyze results.
  • Experience with direct use of lab equipment (e.g., scopes, analyzers, or oscilloscopes).
  • Experience with low-speed interfaces (e.g., SPI or I2C) and high-speed SerDes interfaces (e.g., Ethernet or PCIe).

Nice to have

  • Master's degree in Electrical/Computer Engineering, Computer Science or a related field.
  • Expertise in running and debugging the PCI Express protocol for root complexes and endpoints.
  • Understanding of signal integrity, especially for high speed interfaces, and best practices to collect accurate SI data.
  • Familiarity with lab equipment like scopes, BERTs and analyzers.
  • Ability to use scripting and automation to execute test algorithms, and then analyze results.
  • Excellent communication skills, both verbal and written.