Computational Research Task Author
Listing checked October 8, 2026 · pay as published by AfterQuery
Overview
AfterQuery builds a bench of researchers who turn their own computational science into evaluation tasks for AI models. You would take a simulation, solver, fitting routine, data-reduction step, or analysis pipeline you have run and shape it into a research-grade problem with subproblems, a reference answer, and tests that check the scientific result. Python serves as the working language, and the scientific depth should come from your domain, whether that means bioinformatics, quantum chemistry, computational astrophysics, or another computational field. Reviewers give structured feedback, and you revise until the task passes approval. The work is task-based, with no fixed hours each week.
What You'll Do7
- 1Design original evaluation tasks from your own computational research, using simulations, solvers, fitting routines, data-reduction steps, or analysis pipelines you have run and debugged.
- 2Define the metrics that a strong solution should meet, while keeping exact passing thresholds out of the published task.
- 3Write a reference solution and test cases that verify the scientific result, not just code output.
- 4Break each task into a main problem and a few connected subproblems, with difficulty rooted in the science rather than missing information.
- 5Revise your tasks in response to structured review feedback until they earn approval.
- 6Review and calibrate tasks submitted by other experts in your domain when you choose to take that work.
- 7Maintain your own schedule and pace, since task volume has no cap and no fixed hours each week.
Requirements7
- 1Enrolled in a PhD program or hold a PhD, with hands-on research underway, or hold a Master's with a research-active record such as a thesis, RA or lab role, publication, or clear PhD path.
- 2Working proficiency in Python, enough to run and debug your own computational work.
- 3Ability to convert your computational research into a well-specified evaluation task with clear inputs, outputs, metrics, and tests.
- 4Comfort with independent, task-based work and no fixed schedule each week.
- 5Current PhD student, postdoc, or faculty member who publishes in your field.
- 6Experience in scientific computing, data analysis, simulation, or numerical methods.
- 7Background in data annotation, data labeling, or AI/ML evaluation.
Who Should Apply
Researchers who can point to computational work they ran themselves will fit this role best: a PhD student with simulation code, a postdoc with a numerical solver, or a Master's student with a thesis pipeline and publication record. The strongest candidates pair domain depth in fields such as computational biology, materials science, or plasma physics with enough Python skill to test and debug their own task design. The role suits people who want paid, flexible task work and can turn a research problem into a clean specification with metrics and tests. Candidates who only have coursework in Python, no hands-on computational project, or no thesis, lab role, or publication will likely score low on fit. Another common rejection reason: submitting a task that hides key details to create difficulty instead of letting the science carry the challenge.
Salary Insight
The listing posts $100.00/hr and $200 per approved task at about two hours each, so the two figures align. Because pay goes per approved task rather than per logged hour, your earnings track how many tasks pass review and how fast you can produce them. The rate fits the PhD-level, research-active profile AfterQuery seeks, and the company sets no cap on task volume.
Pay and demand for AI Training roles
AggregatedTypical pay
$70/hour
This role
$100/hr
Most AI Training roles pay $37–$95 per hour. This role's pay sits above that range.
Based on 957 similar roles that publish pay · 300 publish only a top rate; those count at the rate they gave
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Most requested skills · share of roles
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- ai evaluation13%
- llm evaluation12%
- data annotation11%
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Compensation
$100/hr
Required Skills
Application Tip
Prepare your full publication list and current institutional affiliation before you apply, since AfterQuery requests both and verifies identity. In the same document, describe one computational project you ran from start to finish: name the solver, dataset, or pipeline, point to the related paper, and state what you did in Python. That specific evidence helps reviewers see that you can produce a research-grade task rather than a generic coding exercise.
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