Staff Image Quality Evaluation Engineer, Silicon
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Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience with image quality evaluation of camera ISP, display subsystems, machine learning based image processing, computational photography, or related fields.
- Experience in subjective and objective image quality evaluation of camera ISP, display subsystems, or related algorithms.
- Experience in image quality assessment tools such as Imatest or similar.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering or Computer Science, with an emphasis on computer architecture.
- 12 years of experience in image quality evaluation of camera ISP, display subsystems, or related fields.
- Experience with camera algorithms and architecture.
- Experience with programming languages such as C/C++ or Python.
- Knowledge of machine learning, deep learning, generative AI models for image/video processing.
- Understanding of image quality metrics.
About the job
Be part of a 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.Responsibilities
- Evaluate camera ISP, display, camera Video ML algorithms and identify areas for improvement to enhance image quality.
- Develop image quality evaluation methodologies for emerging Video ML and Generative AI use cases.
- Conduct objective and subjective image quality assessment to identify image artifacts, noise, and other image quality issues, then propose improvements.
- Collaborate with cross-functional teams to define and address image quality issues throughout the development process.
- Maintain image quality standards and testing methodologies to ensure consistency and repeatability of image quality evaluations.
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