Image Processing Engineer
Listed on 2026-07-10
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Engineering
AI Engineer (Applied/Software), Computer Science
Image Processing Engineer
Driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
Minimum qualifications:
- Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, Physics, or a specialized field (e.g., optics, sensors, audio, or DSP), or equivalent practical experience.
- 2 years of experience working in an image quality, computer vision, or related technical environment.
- Experience with Python and C++ for algorithm development and implementation.
Preferred qualifications:
- Master's degree or PhD in Electrical Engineering, Computer Engineering, Physics, or a related field (e.g., optics, sensors, audio, or DSP).
- Experience with emerging technologies and new technical domains.
- Ability to solve multi-layered technical problems on their own with a high degree of motivation.
- Excellent written and verbal communication skills, with a demonstrated ability to translate technical concepts into clear, actionable insights for cross-functional partners.
About the job
The Platforms and Devices team encompasses Google's various computing software platforms across environments (desktop, mobile, applications), as well as our first party devices and services that combine the best of Google AI, software, and hardware. Teams across this area research, design, and develop new technologies to make our user's interaction with computing faster and more seamless, building innovative experiences for our users around the world.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $132000 - $190000 (USD) + 15% bonus target + equity + benefits.
Responsibilities
- Optimize image quality across the hardware and software stack, ensuring hardware capabilities are leveraged through software tuning.
- Fine-tune 3A algorithms to ensure performance across lighting and environmental conditions.
- Build software tools, utilizing machine learning (ML) automation to streamline image quality (IQ) tuning, testing, benchmarking, and calibration workflows.
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