Research Scientist

The Stanford Institute for Human-Centered Artificial Intelligence (HAI) is an established research institute within the Dean of Research. As an independent research institute, HAI’s mission is to advance AI research, education, policy and practice to improve the human condition. The AI Index is an independent initiative at the Stanford Institute for Human-Centered Artificial Intelligence. It is guided by the AI Index Steering Committee, an interdisciplinary group of experts from across academia and industry, and produced by a dedicated research team.

The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide independent, rigorously vetted, and globally sourced data and analysis that helps policymakers, researchers, industry leaders, journalists, and the public understand AI’s trajectory and make informed decisions.

Reporting to the AI Index Research Manager, the research scientist will maintain and enhance the Global AI Vibrancy Tool and support the research behind the annual AI Index report, including monitoring new research in the field, collecting and validating data, consulting outside experts, and writing technical sections of the report. In this role, there will be the opportunity to help the Index measure the state of AI more fully by surfacing gaps, trends, and new measurement opportunities.

We are hiring one (1) full time (100% FTE), benefits-eligible, fixed-term research scientist for two (2) years. Potential for an extension following initial 2 years, contingent upon additional funding commitments and/or programmatic needs.

Work Schedule:

Hybrid work arrangement

$108,002-$128,138

• Graduate degree in a quantitative or technical field, or equivalent depth of relevant experience
• Working knowledge of the current AI landscape, including foundation models, evaluation and benchmarking, and how AI progress is measured
• Proficiency with statistical analysis and data visualization tools such as Python, R, SQL, Tableau, or comparable platforms.
• Experience working with large, messy datasets from multiple external sources, including reconciling different formats and definitions
• Strong judgment about data quality, comparability, and when sources can and cannot be compared
• Clear technical writing, with a track record of published or public-facing analytical work
• Self-directed and comfortable with ambiguity in a fast-moving field