Scientific Researcher (Computational Task Author)

$60/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Location not stated
Field: Sciences

Turn computational work from your own research into scientific evaluation tasks for AI models. You will write problems, reference solutions and tests, then revise submissions through structured review.

The work

You will adapt computational work you have personally run or debugged into research-grade evaluation tasks. Suitable source material includes simulations, numerical solvers, model fitting, data reduction, analysis pipelines and inverse problems. Each task contains a main problem, connected subproblems, a reference solution and tests that assess the scientific result.

Evaluation design

You will define scientifically meaningful metrics without publishing exact passing thresholds. Tasks should be difficult because of the underlying science, not because instructions, inputs or assumptions are missing. Submissions go through structured review and may require revisions; reviewing and calibrating other researchers' tasks against a short rubric is optional.

What the platform screens for

Eligible researchers are active PhD researchers or research-active Master's students or graduates with the stated research record. Working proficiency in Python is required, including the ability to run and debug your own computational work. The screen is likely to examine one concrete research workflow in depth, how you would make it reproducible and testable, and how you distinguish model failure from ambiguity in the task.

Practical details

This is remote, flexible independent-contractor work with no fixed weekly schedule or cap on task volume. A task typically takes about two hours, although review and revision needs may vary, and accepted experts undergo standard background and identity verification. The observed band is not guaranteed and may depend on task approval and platform terms.

Pay band: $60/hr

What they look for

Active PhD-level research or a research-active Master's record meeting the stated pathway
Working proficiency in Python for running and debugging computational research
Hands-on experience with scientific computing, simulation, numerical methods or data analysis
Ability to convert original research work into a complete, well-specified evaluation task
Ability to create reference solutions, scientific metrics and result-focused tests
Independent task management and revision in response to structured review

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This checks whether your education and research activity fit an eligible pathway and whether you can identify a real computational workflow rather than discuss a field only at a conceptual level.
A strong answer: State your degree or role, research setting and dates, then identify the scientific question, code or software used, inputs, outputs and your direct responsibility. Mention a verifiable artifact such as a thesis, publication, repository, analysis result or lab project where appropriate.
Where people lose it: Candidates lose points by naming a broad research area without separating their own computational contribution from work performed by a supervisor or collaborator.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.