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AfterQuery is seeking literary scholars as contractors to author and grade close reading scenarios used to test model judgment. You will craft and reference readings, ground interpretations in textual evidence, and assess fidelity, depth, and craft of model outputs.
Fully remote, async, with no fixed hours, minimum 10 hours per week and weekly payments via Stripe. Your rate is set by experience; ongoing project work rather than a single task.
Literary scholars to build the close reading and interpretation scenarios frontier AI labs use to test model judgment. AfterQuery builds the expert evaluation data that frontier AI labs use to measure and improve their models. We're hiring literary scholars as contractors to author and grade the close reading and interpretation scenarios those evaluations run on.
Author literary scenarios that test real reading, from passage analysis to questions of form, voice, and tradition. Write reference readings with the textual evidence behind each claim, what the language does and how. Grade model output for textual fidelity, interpretive depth, and awareness of craft. Flag readings that sound sophisticated but ignore what the text actually says, the misses only a trained reader would catch.
Literary scholars with an advanced degree in literature, English, comparative literature, or a closely related field. Teachers, researchers, editors, and published critics with at least two years of professional literary work. Readers who can ground an interpretation in the words on the page and know when one cannot be. Writers whose own prose demonstrates the craft they evaluate.
$50 to $100/hr, paid weekly via Stripe. Your rate is set from your experience, not negotiated down after you start. Fully remote, fully async. No fixed hours. Minimum 10 hours per week, no maximum. Scale up or down week to week. No screen recording, no keystroke monitoring, no idle timers. Independent contractor. Ongoing project work rather than single task batches.
AfterQuery is an expert data lab. We build the evaluation and training data that frontier AI labs use to measure what their models can and can't do in real professional work. Our contributors are practicing professionals, not annotators, and the work is credited, reviewed, and paid at professional rates.