Imaging Machine Learning Engineer

Imaging Machine Learning Engineer

Google

Mountain View, CA, USA

Mid

Experience 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 Science, Imaging Science, Physics, or a related field, or equivalent practical experience.
• 2 years of experience in Image Quality, Computer Vision, or a related technical field.
• 2 years of experience in Python and C++ for algorithm development and implementation.

Preferred qualifications:

• Master’s degree, or PhD in a related field.
• 3 years of professional experience in a related field.
• Industry experience in SoC/ISP constraints, 3A tuning, or the application of Deep Learning to real-time imaging pipelines.
• A proven track record of delivering high-quality, commercial, market-facing consumer cameras.
• A strong track record of self-driven learning and the capability to rapidly master new technologies and domains.
• 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 - $189000 (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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