Job Description

### Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or equivalent practical experience.
• 3 years of experience with GPU microarchitecture memory sub-system and scheduling mechanisms.
• Experience with programming languages such as C, C++, or Python.



### Preferred qualifications:

• Master's degree in Electrical Engineering, Computer Engineering, or Computer Science, emphasizing on computer architecture.
• Understanding of system-level interactions between the GPU, CPU, memory and other components, including knowledge of interconnect.
• Deep understanding of processor design principles, pipeline optimization, memory hierarchies, Instruction Set Architectures (ISA), and microarchitecture concepts.
• Familiarity with operating system concepts like memory management, scheduling and device drivers, especially for GPU drivers.
• Familiarity with tools to generate thread level parallelism reports.
• Proficiency in profiling tools and techniques to identify bottlenecks and optimize GPU performance.

About the job
-----------------


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.

Responsibilities
--------------------

• Assess performance analysis infrastructure, which includes preparing workloads, developing performance models, and conducting performance and workload analysis.
• Participate in Performance, Power, Area (PPA) tradeoff analysis for architecture and microarchitecture features. Communicate analysis results in both qualitative and quantitative fashion to support major decisions.
• Assess performance analysis infrastructure, which includes preparing workloads, developing performance models, and conducting performance and workload analysis.
• Work with the RTL team to correlate the performance model with the RTL.
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.