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AfterQuery is hiring materials scientists and engineers as independent contractors to author and grade selection and failure analysis scenarios for frontier AI evaluations. This is a remote, fully async role with no fixed hours and ongoing project work, paid weekly via Stripe at $100–$130/hour.
Minimum 10 hours per week, with flexible hours and long-term engagement. Ideal candidates hold a degree in materials science and engineering or related field, 2+ years in materials engineering or R&D, and
Materials scientists and engineers to build the selection and failure analysis 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 materials scientists and engineers as contractors to author and grade the selection and failure analysis scenarios those evaluations run on. What you'll actually do Author realistic materials scenarios, from selection through failure analysisWrite reference answers with the structure property reasoning behind each decisionGrade model output for technical accuracy and practical feasibilityFlag recommendations that read plausible but would fail in service Who this is for Scientists and engineers with a degree in materials science and engineering or a closely related fieldPractitioners with at least 2 years of professional materials engineering or R&D experienceSpecialists with depth in at least one materials class such as metals, polymers, ceramics, or semiconductorsClear writers who can document the reasoning behind a materials decision Contract and pay $100 to $130/hr, paid weekly via Stripe. Your rate is set from your experience, not negotiated down after you start.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. About AfterQuery 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.