Research Scientist, Graph Neural Networks, Omega
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Minimum qualifications:
- PhD degree in Computer Science, a related field, or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages (e.g., Python, C, C++, Java, JavaScript), including application of data structures and algorithms
- Experience in Machine Learning (Graph Convolutional Networks, Deep Neural Networks, Transformers etc.) or related fields.
- Contribution to research communities or efforts, including publishing papers at conferences (such as NeurIPS, ICML, CVPR, SIGGRAPH etc.).
Preferred qualifications:
- Experience in large language models or graph mining research.
- Experience in research leadership.
- Proven track record of publication history at machine learning (ML)/data mining conferences with emphasis on our research area (such as GNNs, geometric deep learning, etc.).
About the job
As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
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.
The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Integrate graph-structured data with foundation models and generative AI.
- Develop novel methods to improve model performance on challenging real world problem settings and working with product teams to get these models out to users.
- Run experiments and document research results for academic conferences.
- Educate Googlers about best practices for learning and reasoning over graph data.
- Develop new data mining pipelines and make improvements to the Graph Mining library as needed.
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Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.
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