At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.
In this role, you will have the opportunity to explore projects at industrial scale, work with technology, and experience Google culture. You will return to the universities with new research directions and educational ideas. You will collaborate on a project proposal that includes expected outcomes and results.Google Research addresses challenges that define the technology of today and tomorrow. From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field -- we publish regularly in academic journals, release projects as open source, and apply research to Google products.
Responsibilities
- In this role you will propose novel ideas and solutions to enable models that learn continuously from user interactions, adapt to new users with minimal data, and can be deployed efficiently on personal devices.
Qualifications
Minimum qualifications:
- PhD degree in Computer Science, Artificial Intelligence, Machine Learning, or related technical field, or equivalent practical experience.
- Experience contributing to research communities and/or efforts, including publishing papers at first-tier academic conferences (such as CHI, UIST, NIPS, ICML, ACL, CVPR, etc).
- Experience with deploying machine learning algorithms to real-world applications.
Preferred qualifications:
- Experience in Deep Neural Networks
- Experience with using generative AI solutions in products and background in ML model development considered a plus.
- Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations