Mathematics Expert (Masters/PhDs)
Alignerr · Remote · Data annotation / labeling
Mathematics Expert (Masters/PhDs) is an open role at Alignerr, available remotely, with pay listed as $75–$90/hr.
Applications open on Alignerr’s own site. How we make money.
At a glance
- Pay
- $75–$90/hrStated by Alignerr
- Location
- Remote
- Work type
- Data annotation / labeling
- Field
- Science / STEM
- Listed
- Since Oct 4, last confirmed Oct 23
How the pay compares
Higher than 55% of the 117 distinct hourly rates advertised for Science / STEM roles right now. Hourly USD, top of each advertised range, one count per distinct rate. Same method as the Pay Index.
From Alignerr’s listing
“Mathematics Expert (AI Training) About the Role What if your years of advanced mathematical training could directly shape how AI reasons through the world's most complex problems?”
Show the full listing
Mathematics Expert (AI Training) About the Role What if your years of advanced mathematical training could directly shape how AI reasons through the world's most complex problems? We're looking for Mathematics Experts — with Master's or PhD credentials — to help build and refine the mathematical reasoning capabilities of cutting-edge AI models. This is a fully remote, flexible contract role. You'll work asynchronously alongside leading AI research teams, applying your deep expertise in algebra, calculus, statistics, discrete mathematics, or related fields to problems that genuinely matter. Organization : Alignerr Type : Hourly Contract Location : Remote Commitment : 10–40 hours/week What You'll Do Develop, solve, and critically review advanced mathematical problems with real-world relevance Design complex, rigorous problem statements across your areas of expertise — algebra, calculus, statistics, discrete mathematics, or related domains Evaluate AI-generated mathematical reasoning for accuracy, depth, and logical soundness Collaborate asynchronously with AI researchers and fellow domain experts to improve model performance Ensure mathematical rigor, clarity, and precision across all deliverables Work independently on task-based assignments at your own pace Who You Are Holder of a Master's or PhD in Mathematics or a closely related field Deep expertise in one or more of: algebra, calculus, statistics, or discrete mathematics Comfortable coding in Python, MATLAB, or Mathematica for research or applied projects Exceptional written communicator — you can explain complex ideas with clarity and precision Detail-oriented, rigorous, and methodical in your approach Fluent in English Nice to Have Prior experience with data annotation, data quality evaluation, or AI training workflows Background in applied mathematics, computational mathematics, or mathematical modeling Experience reviewing or editing mathematical content for publication or instruction Familiarity with AI tool
About Alignerr: part of Labelbox. See the full profile →
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Frequently asked questions
How do I apply for the Mathematics Expert (Masters/PhDs) role at Alignerr?
Applications go through Alignerr's own portal - HumanSourcer links to the listing and never collects applications or handles hiring. Alignerr's access model is "Open / self-serve" (Open roles / applications). This listing was first seen here on October 4, 2026 and was still live at the last check.
Is the Mathematics Expert (Masters/PhDs) role at Alignerr remote?
Yes - Alignerr lists this role as remote.
What does the Mathematics Expert (Masters/PhDs) role at Alignerr pay?
Alignerr lists this role's pay as $75–$90/hr, quoted from the listing rather than estimated.
What kind of work is Mathematics Expert (Masters/PhDs)?
Data annotation / labeling. AI training, evaluation, coding, writing and specialist review
Is Alignerr a legitimate AI-training platform?
Labelbox expert network - and that relationship is rated "Confirmed" here because it can be checked against labelbox.com rather than taken on Alignerr's word. Confidence describes how well the ownership is evidenced. It is not a rating of how the platform treats the people who work for it, which no public source covers reliably.
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