About the job

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

Pixel Camera team aspires to deliver the world's best mobile camera and camera experiences through software innovation. It has consistently landed huge, publicly visible impact by pushing the limit of mobile photography and videography with each year's Pixel smartphone launches.

The Google Pixel team focuses on designing and delivering the world's most helpful mobile experience. The team works on shaping the future of Pixel devices and services through some of the most advanced designs, techniques, products, and experiences in consumer electronics. This includes bringing together the best of Google's artificial intelligence, software, and hardware to build global smartphones and create transformative experiences for users across the world.

Responsibilities

• Develop and sustainable AI/ML methods to solve key problems in autofocus for on-device applications, such as detection, segmentation, tracking, depth estimation, and saliency.
• Train and deploy server-side models (e.g., convolutional neural network, transformers, etc.) for distillation and evaluation. Explore Generative AI use cases, such as auto-labeling.
• Build data and evaluate pipelines for novel machine learning problems. Optimize and integrate machine learning models for on-device deployment. Create and execute end-to-end evaluation plan to ensure quality and robustness.
• Work closely with cross-functional teams on machine learning problems. Leverage and drive partner teams for integration, testing, and optimization. Communicate plans, progress, and results effectively for an audience with or without machine learning background.