Students Across All Disciplines (Undergrad) - AI Trainer

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.

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.

Responsibilities

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)

Required Qualifications

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

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 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)

About AfterQuery

AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We believe great AI comes from exceptional, human-generated data.

We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.

Apply today to contribute to pushing the frontier of AI.

Apply here: https://experts.afterquery.com/apply/students-opportunity