Research Fellowship in AI and the Law

Note: The expected pay range for this position at Stanford Law School is $29.52 to $34.13 per hour. Stanford University provides pay ranges representing its good faith estimate of the hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The full-time position is fixed-term for one year, with the intention to renew for a second year, subject to approval. It is expected that the fellow will be physically present in and working from Stanford Law School. This full-time research fellowship provides opportunities to assist the Legal Innovation through Frontier Technology Lab (liftlab), jointly led by Professor Julian Nyarko and Dr. Megan Ma, in its research. The Fellow will support the execution and administration of research activities on AI and other legal technological developments; and make recommendations on project development and implementation. The fellowship is aimed at recent graduates who are considering entering a graduate degree program (Ph.D. / JD) in the near future. The first two fellows have gone on to pursue PhDs at MIT and Harvard, respectively, as well as a JD at Stanford. Examples of Current Projects Improving contract drafting practices by identifying litigation-triggering language Developing a benchmark to compare rubrics and preference ranks as a method to elicit quality in high-judgment domains Developing and experimentally testing LLM-based interventions to improve negotiation outcomes LLM personalization to improve downstream outcomes Large-scale evaluation of AI tutors for law Developing a methodology to predict future innovations in the social sciences Auditing and mitigation of bias in LLMs via pruning Responsibilities Designing methods to validate data to ensure high quality output Identifying new sources of data and methods to improve data collection, analysis and reporting Conceptualization and implementation of statistical models Collecting, managing, and structuring quantitative datasets Report writing and manuscript preparation Using LLM agents for the implementation and execution of research+ Preferred Qualifications: A Bachelor’s degree in a relevant field Experience with statistics Programming experience Ability to work under deadlines with general guidance Experience and knowledge of the following is highly desirable: Highly Desirable: A Bachelor’s degree in computer science with a background in NLP, a Bachelor’s degree in (computational) linguistics or in symbolic systems A strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning Substantial experience with Python Substantial experience with R #J-18808-Ljbffr SLAC