Senior Research Scientist, Interactive Recommender Systems, Google Research
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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, 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.
Our team is focused on research to develop the next generation of technologies to power systems and other user-facing products at Google, with an emphasis on 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, using techniques such as reinforcement learning; personalization via user preference modeling and elicitation; and modeling multi-agent interactions, incentives and content creation using game theory and mechanism design. We collaborate directly with product teams to both inspire and apply new research directions.
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 $161,000-$239,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target salaries for the position across all US locations. 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
- 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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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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