Mathematics Quality Assurance Lead (QAL)

In this hourly, remote contractor role, you will work as a Mathematics Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across mathematics AI training projects. You will review AI-generated math content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for mathematical accuracy, logical reasoning, calculation correctness, proof validity, notation quality, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong mathematics expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your mathematics quality leadership will directly help improve the world’s premier AI models by ensuring that math training data is accurate, logically sound, clearly explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Your profile

• 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, WolframAlpha/Mathematica, GeoGebra, 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.

Key responsibilities

• 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.