Research Scientist, Market Algorithms, Google Research - Google
Mountain View, CA
About the Job
Minimum qualifications:
- PhD degree in Computer Science, Theoretical Computer Science, Economics, Operations Research, Machine Learning or a related field, or equivalent practical experience.
- One or more scientific publication submission(s) for conferences, journals, or public repositories covering relevant research topics.
- Experience publishing in top research conferences.
Preferred qualifications:
- 2 years of coding experience.
- 1 year of experience owning and initiating research agendas.
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.
In this role, you will conduct research at the intersection of microeconomics, algorithms, and machine learning to design economically and computationally efficient marketplaces across Google, from Ad Auctions to Platform Ecosystems. You will work closely with Product team partners to take research all the way to impact. You will publish research at top conferences in these areas, and present and engage with the research community.
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 $136,000-$200,000 + 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
- Conduct research in areas such as mechanism design, auctions, optimization, game theory, and other related ML topics.
- Deliver research all the way to impact, engaging closely with partner teams.
- Publish research at top conferences / journals in these areas, and present and engage with the research community. Sample conferences include (but not limited to): EC, SODA, STOC, FOCS, Neurips, ICML, AAAI, IJCAI, WINE, Webconf.
- Conduct practical algorithm and auction design, data analysis, ML, simulations.
- Understand important problems in practice and model theoretically.