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. Responsibilities Apply your domain expertise to evaluate, write, and validate content that requires genuine academic knowledge in your fieldAssess materials for accuracy, depth, and quality from the perspective of a trained specialist in your disciplineIdentify errors, ambiguities, or gaps that would be invisible to a non-expert reviewerProvide structured written rationale and feedback for each evaluation decisionInterpret 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) Required Qualifications Currently a junior or senior undergraduate at a research university, with strong academic standing in your fieldAbility to produce clear, well-reasoned written explanations of domain-specific judgmentsFamiliarity 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 workStrong English writing proficiency Preferred Qualifications 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 experienceStrong academic standing in a top program in your disciplineFamiliarity with AI tools, large language models, or multimodal systems, even at a general levelQuantitative methods training (relevant across science, social science, business, and engineering tracks) Why apply Strong compensation Earn $800–$1,500 per project week depending on discipline and seniority, with top candidates eligible for premium rates.Intellectually engaging work Your domain knowledge directly shapes how AI systems understand your field - not busywork, but substantive expert evaluation.Fully flexible schedule Asynchronous work you fit around your academic commitments. Accept only the projects that work for your schedule.