Senior Research Scientist, Interactive Recommender Systems, Google Research

Minimum qualifications: PhD degree in Computer Science, a related field, or equivalent practical experience. 2 years of experience leading a research agenda. Research experience with one or more of the following: multi-agent systems, algorithmic mechanism or market design, recommender systems, reinforcement learning or active learning, generative AI. One or more scientific publication submission(s) for conferences, journals, or public repositories. Preferred qualifications: 2 years of coding experience. 1 year of experience leading research efforts and influencing other researchers. Practical experience with Machine Learning methods. Programming experience in C, C++, or Python. Strong publication record. 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. As a Research Scientist, you'll set up 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. Our research scientists work on real-world problems that span the breadth of computer science, such as machine learning, data mining, natural language processing, hardware and software performance analysis, and 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 worldwide. Our team focuses on research to develop the next generation of technologies to power systems and user-facing products at Google, emphasizing maximizing long-term user satisfaction in complex ecosystems. We pay special attention to interactive systems, such as conversation recommender systems, and the use of generative models; long-term sequential optimization; personalization via user preference modeling; and modeling multi-agent interactions using game theory and mechanism design. Google Research addresses challenges that define the technology of today and tomorrow. Our research teams have the opportunity to impact technology used by billions of people every day. Responsibilities: Pursue work and research at the intersection of two or more of the following topics: recommender systems, mechanism/market design, multi-agent modeling and optimization, user modeling and simulation, interactive systems, generative AI models, reinforcement learning. Design and implement scalable machine learning and optimization techniques to solve real-time problems in user-facing, interactive systems (e.g., recommender systems). Pursue a combination of research (with the opportunity to publish) and technology development and innovation in real products. Work with other researchers and developers in collaborative teams.

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