Industry Job Opportunity
Position Type: Full-time, multiple openings
Locations: primarily Raleigh, Durham and Sunnyvale locations
The Systems and Services Infrastructure Performance team at Google develops the methodology for the high performance architecture and design of all systems deployed in Google data centers (servers, storage, networking, machine learning systems based on TPUs, transcoding accelerators, etc.). When you search on Google.com, watch a video on YouTube or read your Gmail, you are using the systems we have helped architect and optimize. These systems host all the applications and services that Google provides to billions of users and the foundation for Google Cloud services.
The team has ambitious plans and will be hiring over 40 engineers this year, primarily in Raleigh, Durham and Sunnyvale locations. Hires for other locations may also be considered on a case by case basis. We need engineers with skills in chip modeling and simulation, distributed systems, performance analysis, machine learning, computer architecture and related areas.
We are expanding our world class team that fearlessly addresses the engineering challenges of architecting, designing and developing systems at planetary scale. Many of the problems we work on require invention, since the existing state of the art is not sufficient. Although we do not always publish our inventions, you can find some of our work in the top Systems and Machine Learning conferences. Some example publications from the team since its inception about 8 years ago can be found here.
Some new initiatives that we are looking to staff are on:
- Google SoC modeling (link to posting); Google builds our own optimized silicon for many workloads. In order to project and architect that silicon, we need detailed performance models. Our team is looking for SWEs who love to work on hardware and can straddle the HW-SW boundaries.
- ML for Systems Efficiency (link to posting) : Our goal is to improve Google’s fleet efficiency by Machine Learning driven analysis to identify hardware and software root-causes of performance degradation and variability and come up with creative ways to address them at Google scale.
- Planet-scale database acceleration (link to posting): (1) optimizing memory and compute efficiency through holistic analysis, modeling, and leveraging new memory tiers and compute platform heterogeneity, and (2) ML-driven auto-tuning of database performance.
- Segment Optimized Computing (link to posting): Embrace heterogeneity as a purposeful design principle for the next generation warehouse computers (WSCs) by designing diverse hardware to match varied requirements of workloads.
Qualifications:
- Successful candidates tend to be either PhD students in Systems / Architecture, or Masters students with a good track record of research experience with Systems/Architecture faculty members.
- We are also looking for students in ML with interest in solving problems in the Systems area using ML. We publish in ICML/NIPS/ICLR (and of course OSDI/ISCA/ASPLOS/etc)
How to Apply
If you are interested in applying for some open roles, please send your resume to begibson@google.com along with the list of openings that match your skills and career aspirations.
Equal Opportunity Statement
At Google, we don’t just accept difference—we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.