PhD Mathematics AI Evaluator

About the role PhD Mathematics AI Evaluator is a remote review track for evaluating AI outputs across mathematics reasoning, calculations, and research workflows. Reviewers grade derivations and assumptions, reproduce key results, and document the correct method so the modeling team can train on it. Mathematics models live or die on whether their derivations actually hold up under scrutiny. AuraOne uses scientific specialists to grade outputs the way a peer reviewer would — checking assumptions, reproducing key steps, and capturing the right method alongside the wrong one. Bring scientific and technical domain expertise into AI reasoning, research, and dataset review. Responsibilities Review AI outputs against current mathematics methods, conventions, and prior work for PhD Mathematics AI Evaluator assignments. Reproduce or sanity-check key derivations, calculations, or experimental claims. Flag dimensional, methodological, and citation errors with structured severity tags. Capture the corrected reasoning or worked example so the modeling team can train on it. Role details Track STEM research review Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US What you should bring Graduate-level training or equivalent applied experience in mathematics or a closely related field for PhD Mathematics AI Evaluator work. Hands-on experience publishing, teaching, or advising on the topic at a professional level. Comfort applying multi-page rubrics consistently across long batches. Clear written reasoning that cites methods, papers, or worked examples. Reliable async availability for at least 10 hours per week. Role signals Example tasks Reproduce a mathematics derivation from a model output and flag any algebraic or dimensional errors. Grade a model's literature summary against the cited papers and rate the citation quality. Adjudicate a disputed answer between two reviewers using textbook methods. Audit a 25-row batch for rubric consistency and report drift to the program lead. Useful experience PhD, postdoc, or industry research experience in the topic area. Prior work reviewing AI-assisted research tooling and its failure modes. Multilingual fluency for non-English papers and corpora. Compensation and schedule Hourly rate confirmed after the interview process. Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation. Skills used in matching Scientific reasoning Method validation Citation review Quantitative analysis Mathematics Science and advanced mathematics AI evaluation Rubric writing Expert review #J-18808-Ljbffr AI Trainer Jobs