Job Information
Google Machine Learning System Tooling Tech Lead, Silicon in New Taipei City, Taiwan
Google welcomes people with disabilities.
Minimum qualifications:
Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
5 years of experience with computer architecture concepts, including microarchitecture, cache hierarchy, pipelining, and memory subsystems.
Preferred qualifications:
Master's Degree or Ph.D. with an emphasis on performance evaluation for Machine Learning (ML) systems.
Experience with ML accelerators (e.g. having worked on ML software models or accelerator architectures).
Experience writing ML algorithms for e.g. recommendation systems, Natural Language Processing (NLP), image and vision.
Experience in tooling development for power, performance and architecture analysis.
Experience in architecting and optimizing compilers.
Understanding of compiler flows, software involved in translating a high-level language (e.g. TensorFlow) to hardware instructions.
Be part of a diverse 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.
Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.
Design, develop, and maintain tools and infrastructure for analyzing Machine Learning (ML) workloads and hardware performance.
Develop and maintain power and performance models.
Develop visualizations and dashboards to effectively communicate performance insights to engineers.
Build models, benchmarks for workload analysis and help to drive architectural decisions.
Collaborate with cross-functional teams to improve the workload analysis flows, including debuggability and tracing.
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. See also https://careers.google.com/eeo/ and https://careers.google.com/jobs/dist/legal/OFCCPEEOPost.pdf If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form: https://goo.gl/forms/aBt6Pu71i1kzpLHe2.