Students Across All Disciplines (Undergrad)

This is a remote, project-based role for strong undergraduates across academic disciplines, to contribute to an AI evaluation research project. You will apply the domain knowledge you've built through coursework, lab work, and research to assess, write, and validate content that requires genuine subject-matter knowledge in your field.

Work is asynchronous, assigned on a project-by-project basis, and structured to respect your existing academic schedule. You choose which projects to accept. Expect an estimated commitment of up to 10 hours per week for each project window you take on. This role offers strong pay, genuine intellectual engagement, and a meaningful addition to your academic and professional portfolio.

What you'll do

• Apply your domain expertise to evaluate, write, and validate content that requires genuine academic knowledge in your field
• Assess materials for accuracy, depth, and quality from the perspective of a trained specialist in your discipline
• Identify errors, ambiguities, or gaps that would be invisible to a non-expert reviewer
• Provide structured written rationale and feedback for each evaluation decision
• Interpret and respond to visual or diagrammatic content specific to your field (e.g. schematics, spectra, maps, clinical images, artworks, data charts)
• Complete a defined set of annotation tasks within each project window (10–20 hrs/week)

Requirements

• Currently a junior or senior undergraduate at a research university, with strong academic standing in your field
• Ability to produce clear, well-reasoned written explanations of domain-specific judgments
• Familiarity with the visual conventions and technical vocabulary of your discipline (e.g. reading spectra, interpreting schematics, analyzing maps, evaluating clinical images, or formal visual analysis)
• Reliable internet access and availability for asynchronous, remote work
• Strong English writing proficiency

Nice to have

• Research experience — a lab, RA position, independent study, or thesis — especially involving image-based or visual data (microscopy, imaging, CAD, GIS, spectroscopy, diagrammatic modeling, archival visual materials)
• Prior annotation, data labeling, or AI evaluation experience
• Strong academic standing in a top program in your discipline
• Familiarity with AI tools, large language models, or multimodal systems, even at a general level
• Quantitative methods training (relevant across science, social science, business, and engineering tracks)