Mathematics Quality Assurance Lead; QAL

Mathematics Expert

Bachelor's, Master's, or PhD degree in Mathematics, Applied Mathematics, Statistics, Physics, Engineering, Computer Science, Mathematics Education, or a closely related quantitative field.

Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear mathematical feedback in English.

3+ years of professional experience in mathematics, teaching, tutoring, research, quantitative analysis, technical writing, curriculum development, problem creation, assessment design, or math-content review.

Strong understanding of core mathematics topics such as algebra, geometry, trigonometry, calculus, linear algebra, discrete mathematics, probability, statistics, number theory, combinatorics, differential equations, and mathematical proofs.

Ability to evaluate math content against detailed rubrics and identify issues such as incorrect assumptions, flawed reasoning, invalid proofs, calculation errors, notation problems, missing steps, hallucinated facts, or incomplete explanations.

Comfortable reviewing both conceptual explanations and step-by-step solutions, including whether each step logically follows from the previous one.

Familiarity with mathematical tools or workflows such as LaTeX, Python, MATLAB, R, Wolfram Alpha/Mathematica, Geo Gebra, Desmos, spreadsheet modeling, or symbolic computation tools is preferred.

Experience leading or supporting remote teams of trainers, annotators, reviewers, educators, technical writers, or QAs is strongly preferred.

Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.

Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.

Experience with AI training, data annotation, large language models, prompt/response evaluation, mathematical content QA, or rubric-based LLM evaluation is a strong plus.

Native fluency in Punjabi

Quality monitoring:
Spot-check mathematics items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.

Mathematical review:
Evaluate AI-generated math explanations, proofs, derivations, calculations, word-problem solutions, diagrams/descriptions, and step-by-step reasoning for correctness and clarity.

Trainer and QA communication:
Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and math-specific review standards.

Question handling:
Respond to trainer/QA questions clearly and promptly, especially around reasoning validity, notation, assumptions, solution methods, proof structure, formatting, and rubric interpretation.

Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.

Documentation:
Create and maintain mathematics project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.

Onboarding and training:
Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and mathematics-specific review requirements.

Quality alignment:
Ensure all trainers and QAs apply mathematics guidelines consistently and understand updates as projects evolve.

Error-pattern analysis:
Identify recurring issues such as skipped reasoning steps, invalid simplifications, wrong formulas, notation inconsistencies, arithmetic mistakes, or answers that are correct but poorly justified.

Process improvement:
Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for mathematics AI training projects.