About the job
The Research Intern will conduct empirical research on how human oversight shapes the economic return of AI-assisted work. This includes designing controlled experiments, developing session-level evaluation frameworks that link inference cost to output quality and human effort, and analyzing how interface design choices affect user confidence, reliance, and decision quality during AI-assisted tasks. The Research Intern will collaborate with the MADE team in the Office of the CTO and prepare a submission-ready research manuscript.
Responsibilities
Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.
Qualifications
Minimum
Currently enrolled in a PhD program in Computer Science, Cognitive Science, Human-Computer Interaction, Information Systems, Behavioral Economics, or a related STEM field. At least 1 year of research experience in one or more of the following: human-AI interaction, decision science, metacognition, AI/LLM evaluation, or computational economics.
Preferred
Experience designing and running human-subjects experiments (IRB-compliant protocols, participant recruitment, controlled task studies). Familiarity with LLM agent architectures, prompt engineering, or multi-step AI orchestration systems. Background in confidence calibration, signal detection theory, or metacognitive measurement. Experience with mixed-methods research combining quantitative behavioral data and qualitative interview analysis. Proficiency in Python and statistical analysis tools (R, pandas, scipy, or equivalent). Published or in-progress research in CHI, CSCW, FAccT, AAAI, NeurIPS, or related venues. Familiarity with inference cost modeling, token economics, or cloud compute pricing.