Scientific Computing Research Expert (Remote)
Listing checked September 22, 2026 · pay as published by AfterQuery
Overview
AfterQuery hands working scientists a research writing job built on their own computational projects. You take a problem you already know well and shape it into an AI evaluation task that measures what a model truly understands. Every task also needs the metrics that separate a sound solution from a shallow one, while the exact passing bar stays private. Python covers anything you need to run or debug yourself. Researchers in the life, physical, and social sciences, along with mathematicians and engineers, all fit the brief.
What You'll Do7
- 1Turn your own computational research into a stand-alone evaluation task another model can attempt
- 2Write the problem statement, inputs, and expected reasoning steps in enough detail for outside reviewers to follow
- 3Set the metrics a correct solution has to meet, while keeping the exact passing thresholds out of the published task
- 4Run your own code in Python to confirm the task is solvable and the scoring holds up
- 5Rework each task until reviewers approve it, using their written feedback as the guide
- 6Take on as many extra tasks as you want, since nothing caps your submissions
- 7Review and calibrate tasks from other experts in your field if you opt into that work
Requirements8
- 1Enrolled in a PhD or already hold one, with research you run hands-on
- 2Or a Master's holder with a thesis, an RA or lab post, a publication, or a clear PhD trajectory
- 3Python skills strong enough to write, run, and debug your own computational code
- 4Able to convert your research into a task with stated success criteria
- 5Comfortable setting your own hours, since the engagement is task-based with no fixed schedule
- 6Preferred: a PhD student, postdoc, or faculty member who publishes
- 7Preferred: background in scientific computing, simulation, data analysis, or numerical methods
- 8Preferred: past work in data annotation, data labeling, or AI and ML evaluation
Who Should Apply
The role suits a PhD-level or PhD-track researcher with a live computational project and enough Python to keep their own code running. Postdocs and faculty who publish in their field tend to move through task design fastest, since they already know where a problem gets hard. Candidates without active research, or whose work sits far from computation, struggle to produce a task with real depth. Submissions also get rejected when the task has no reproducible code behind it, or when the author publishes the passing thresholds that should stay hidden. A Master's researcher can qualify, but only with a thesis, an RA position, a publication, or a documented PhD path to show for it.
Salary Insight
Pay runs at $300 per approved task, and each task takes about 4 hours, which works out near $70 to $80 an hour for completed work. Volume is yours to set, so total earnings follow how many tasks you write and get approved rather than any weekly minimum.
Pay and demand for Machine Learning & AI roles
AggregatedTypical pay
$75/hour
This role
$70–$80/hr
Most Machine Learning & AI roles pay $55–$100 per hour. This role's pay falls inside that range.
Based on 513 similar roles that publish pay · 90 publish only a top rate; those count at the rate they gave
Rates shown per hour. Yearly and monthly pay converted; one-time fees and non-USD pay are not included.
- Live similar roles
- 570
- Listed in last 30 days
- 252
- Remote
- 97%
Hiring most right now: micro1 (262) · Mercor (79) · SME Careers (60)
Most requested skills · share of roles
- python17%
- technical writing9%
- llm evaluation8%
- data annotation7%
Figures from Machine Learning & AI roles live on NearSkill when this page loaded. A role can close before you apply, so check the listing itself.
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Compensation
$70–80/hr
Required Skills
Application Tip
Send a cover letter that names your field, your institution, and the computational project you plan to build tasks from, then attach the full list of your published papers and a portfolio piece with code a reviewer can run. Naming specific methods, tools, and any prior annotation or ML evaluation work gives the fit score a direct boost.
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