Job Type
Full Time
Job Details
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 analyzing workloads on real systems or devices.
- Experience in hardware performance monitors or profiling, and profiling or tracing tools in Android such as perfetto, trace or simpleperf.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering, or Computer Science.
- Experience in performance and power optimization.
- Experience with bandwidth and latency analysis, scheduler, DVFS governor, power analysis, thermal management, device driver, OpenCL, or On-device ML.
- Experience with CPU, GPU, NPU, or DSP.
About the jobGoogle'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
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. - Manage performance, power, thermal analysis results both qualitatively and quantitatively.
- Study the behavior and characterization of a variety of workloads on devices for hardware/software co-optimization.
- Participate in both evaluations of future ASIC architecture and software optimization.
- Manage performance analysis for on-device machine learning features.
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