Photonic Engineer, Machine Learning

Google Google · Big Tech · Sunnyvale, CA +1

This role focuses on designing and developing custom silicon solutions, specifically optical transceivers and systems for data center applications, to improve the performance, efficiency, and integration of Google's hardware infrastructure. The engineer will work on scaling TPU and GPU superpods, exploring emerging photonic networking technologies, and improving the efficiencies of future computing systems.

What you'd actually do

  1. Design low-cost and high-speed optical transceivers for data center applications.
  2. Develop optical systems and architectures to scale TPU and GPU superpods.
  3. Identify, research, evaluate emerging photonic networking technologies.
  4. Explore optical technologies to disaggregate and improve the efficiencies of future computing systems.

Skills

Required

  • Bachelor's degree in Electrical Engineering, Computer Engineering, Physics, a related field, or equivalent practical experience.
  • 6 years of experience working in a high-speed datacom technical environment.
  • 5 years of experience in digital coherent optical transmissions.

Nice to have

  • PhD in Electrical Engineering, Computer Engineering, Physics, a related field, or equivalent practical experience.
  • Experience with script writing and test and measurement system automation.
  • Proficiency in digital signal processing, high-speed optoelectronic circuit simulation or system modeling.
  • Knowledge of large volume high-speed optical transceiver designs and manufacturing.
  • Knowledge of data center network and machine learning system architectures.

What the JD emphasized

  • custom silicon solutions
  • hardware and software technologies
  • custom hardware designed and made in-house
  • high-volume manufacturing
  • optical transceivers for data center applications
  • optical systems and architectures to scale TPU and GPU superpods
  • emerging photonic networking technologies
  • optical technologies to disaggregate and improve the efficiencies of future computing systems