Scientific Computing Research Expert

AfterQuery is building a network of PhD-level and PhD-pursuing researchers to author and calibrate research-grade evaluation tasks for AI models.

A strong task is a real piece of computational work from your own research, something you have actually run, debugged, or been paid to do: data reduction, model fitting, simulation setup, instrument data processing, numerical solvers, or inverse problems. The difficulty should come from the science itself, not from artificially withheld information.

We are commissioning tasks across five broad areas: Life sciences (biology, ecology, medicine, neuroscience), Physical sciences (astronomy, chemistry, materials science, physics), Earth sciences (atmospheric, environmental, geosciences, ocean), Mathematical sciences (applied mathematics, formal mathematics, operations research, statistics), and Engineering sciences (chemical, civil, electrical, mechanical).

Domain: Scientific Computing (multi-discipline research task authoring) Experience: Active, hands-on research or technical experience in your field Education: Currently pursuing or holding a PhD, actively doing hands-on research. A Master's also qualifies if you are research-active: thesis-based enrollment, an active RA or lab role, a real publication, or a clear PhD trajectory. A Master's followed by a purely industry role with no research component is not a fit. Technical: Working proficiency in Python. You will need to run and debug your own computational work to author a task.

This is flexible, independent-contractor work: $300 per approved task, roughly 4 hours of work each, with no cap on how many tasks you can take on. Tasks go through review before approval, and most take a round or two of revision to get there. Feedback is specific and you can resubmit.

All accepted experts undergo standard background and identity verification.

Responsibilities

• 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

Required qualifications

• Currently pursuing or holding a PhD with active hands-on research, or a Master's with a research-active record: thesis-based enrollment, an RA or lab role, a real publication, or a clear PhD trajectory
• Working proficiency in Python, enough to run and debug your own computational work
• Able to turn your own computational research work into a well-specified evaluation task
• Able to work independently and manage your own time. This is flexible, task-based work with no fixed weekly schedule

Preferred qualifications

• Current PhD student, postdoc, or faculty actively publishing in your field
• Demonstrated experience in scientific computing, data analysis, simulation, or numerical methods
• Prior experience in data annotation, data labeling, or AI/ML evaluation work

Why apply

• Paid, flexible work: $300 per approved task, no cap on how many you take on
• Work on your own schedule: task-based, not a fixed hourly commitment
• Contribute directly to how AI models are evaluated on real, expert-level scientific work