TPU Architect, Devices and Services, Silicon

작업 내용

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Minimum qualifications:

• Bachelor’s degree in Electrical Engineering or Computer Science, or equivalent practical experience.
• 2 years of experience working in Software development, Computer Architecture or ML accelerators.
• Experience with Python and C or C++.
  
  

Preferred qualifications:

• Master’s or PhD in Computer Science, Electrical Engineering or related technical field.
• Experience with ML Accelerators (e.g. having worked on complex ML Software models or accelerator architectures).
• Experience writing Machine Learning algorithms (e.g. a good understanding of recommendation systems, NLP).
• Experience architecting and optimizing compilers.
• Experience with software development in one or more programming languages, and with data structures/algorithms.
• Understanding of compiler flows software involved in translating a high-level language (e.g. TensorFlow) to hardware instructions.
  
  

About The Job

Our computational challenges are so big, complex and unique we can’t just purchase off-the-shelf hardware, we’ve got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google’s services. As a Hardware Engineer, you design and build the systems that are the heart of the world’s largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems 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.

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.

Responsibilities

• Improve the compiler flows and related tools for architectural analysis.
• Build up the data analysis, visualization tools and dashboards for TPU PPA bottleneck analysis.
• Analyze important Machine Learning (ML) workloads, evaluate power and performance and propose architecture or compiler improvements.
• Collaborate with other teams (Hardware, Compiler, ML researchers) to improve the end to end flow and results.
  
  

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 Google’s EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form .