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Photonic Engineer, Machine Learning
GoogleAustin, TX, USA6+ years
Apply Minimum qualifications:
- 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.
Preferred qualifications:
- 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.
About the job
Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.
Our Platforms Infrastructure Engineering team designs and builds the
hardware and software technologies that power all of Google's services. Our computational challenges are complex and unique, enabled by cutting-edge custom hardware designed and made in-house. As a hardware engineer, you will design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You will see those systems from concept all the way through to high-volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers, affecting millions of Google users.
US: $188000 - $275000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Design low-cost and high-speed optical transceivers for data center applications.
- Develop optical systems and architectures to scale TPU and GPU superpods.
- Identify, research, evaluate emerging photonic networking technologies.
- Explore optical technologies to disaggregate and improve the efficiencies of future computing systems.
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