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Computational Mathematics Researcher, Python Scientific-Computing Task Author (Remote, Contract, $200/approved task) — AfterQuery via AI Gig Scout

AfterQuery (via AI Gig Scout)
1 hour ago
Contract
Remote
Worldwide

Computational Mathematics Researcher (Task Author) — AfterQuery via AI Gig Scout

Pay: $200/approved task · Location: Remote · Type: Contract

Full listing and apply link: https://aigigscout.com/jobs/afterquery/computational-mathematics-researcher-task-author-computational-mathematics-researcher?utm_source=kubernetesjobs&utm_medium=jobboard&utm_campaign=aigigscout_growth


Turn computational work from your own math research into paid evaluation tasks for AI models. Open to research-track Bachelor's students, Master's and PhD students, postdocs, and recent graduates.

AfterQuery is building a network of researchers to author and validate research-grade scientific-computing tasks for AI models.

A strong task is a real piece of computational work from your own research, something you've actually run and debugged: a numerical solver, an optimization routine, a simulation, a statistical model, or an algorithm you implemented and validated. Each task has a main problem, a few connected subproblems, a Python reference solution, and tests that check the result. The difficulty should come from the mathematics itself, not from withheld information.

We're looking for researchers in: numerical analysis and scientific computing; numerical linear algebra; differential equations and dynamical systems; optimization and operations research; probability, stochastic processes, and uncertainty quantification; computational statistics; discrete mathematics and algorithms; computational geometry, topology, and number theory; and inverse problems.

Requirements

Education: A Bachelor's student on a research track or with research experience (such as an REU, a thesis, or an RA or lab role), a Master's or PhD student, a postdoc, or a recent graduate in mathematics, applied mathematics, statistics, or a closely related field.

Technical: Hands-on experience writing your own code, preferably Python, to implement or validate mathematical methods. Familiarity with Python or machine learning alone isn't enough.

Work authorization: Must be able to work as an independent contractor without visa sponsorship. Students on F-1 visas (including OPT/CPT) are not eligible.

Contract and pay

Flexible, independent-contractor work: $200 per approved task, about 2 hours each, with no cap on how many tasks you take on. Tasks go through review, and you may also review other experts' tasks against a short rubric.

All accepted experts undergo standard background and identity verification.

What you'll do

Design and author original research-grade evaluation tasks based on your own computational work

Specify the metrics a strong solution should be judged by, without publishing the exact passing thresholds

Revise tasks based on structured review feedback until they're approved

Optionally review and calibrate tasks submitted by other experts in your domain

Requirements

Bachelor's student on a research track or with research experience, Master's or PhD student, postdoc, or recent graduate in mathematics, applied mathematics, statistics, or a closely related field

Has written their own code, preferably in Python, to implement or validate mathematical methods

Able to turn your own computational research into a well-specified task with a reference solution and tests

Able to work independently and manage your own time. This is flexible, task-based work with no fixed weekly schedule

Preferred qualifications

Research experience in numerical methods, optimization, stochastic processes, or computational topology or number theory

Public code, a thesis, or papers that show your computational work

Prior experience in data annotation, data labeling, or AI/ML evaluation work

Source: AI Gig Scout job page. Apply there for the original platform listing. Disclosure: posted by AI Gig Scout, an independent listing site (not AfterQuery); the apply link may be a referral link.