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X In most instances, this position requires in-person interviews as part of the hiring process.
Minimum qualifications: - Bachelor's degree or equivalent practical experience.
- 5 years of coding experience in C/C programming.
- 3 years of experience with Android application development.
Preferred qualifications: - Experience designing, implementing, and debugging computer vision, image processing, signal processing, or computational photography algorithms.
- Experience in imaging related development on CPU, GPU, or DSP.
- Experience in digital image processing, digital signal processing, and linear algebra.
- Knowledge of imaging pipeline or computational photography.
- Demonstrated interest in photography and videography.
About the jobYou will design and ship algorithms that directly shape perceived photo quality in the Pixel HDR pipeline - the processing that turns merged sensor data into the photo users see. This includes detail enhancement and sharpening, upsampling for zoom, semantic-aware enhancement driven by on-device ML, the tuning framework that adapts these algorithms for new sensors and lenses each Pixel device, and bring them to production on constrained mobile hardware. You will work across C , domain-specific image-processing languages, GPU compute, and ML frameworks, and partner with hardware and application teams to land features on the annual Pixel schedule.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $252000 (USD) 15% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Design, prototype, and deploy innovative computational photography, image signal processing, and machine learning algorithms to advance the mobile camera imaging pipeline.
- Develop and optimize algorithms for detail and texture rendering, sharpening, upsampling, and optical or sensor artifact mitigation.
- Bridge classical image processing and deep learning by developing differentiable imaging components for end-to-end neural network training and hybrid on-device inference.
- Optimize and integrate algorithms into production software across mobile compute architectures (CPU, GPU, DSP, and custom neural accelerators) to meet strict latency, memory, and power targets.
- Collaborate with cross-functional teams (including hardware, optics, silicon, systems, research, and image quality evaluators) to co-design future camera architectures, enable new capabilities, and validate algorithms for consistent, high-precision image quality across sensors.
