Scientific Computing Research Expert

Job summary

Create original, research-grade AI evaluation tasks based on computational work you have personally performed, and define metrics for judging strong solutions. Revise tasks in response to structured review feedback and optionally review or calibrate tasks submitted by other experts.

Job details

Author research-grade AI evaluation tasks from your own computational work. Open to PhD-level and PhD-pursuing researchers across the sciences, math, and engineering. 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). Requirements 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. Contract and pay 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

What you’ll do

Create original, research-grade AI evaluation tasks based on computational work you have personally performed, and define metrics for judging strong solutions. Revise tasks in response to structured review feedback and optionally review or calibrate tasks submitted by other experts.

Requirements

Applicants must be pursuing or hold a PhD with active hands-on research, or have a research-active Master's record, and must be proficient enough in Python to run and debug their computational work. They must be able to turn research into well-specified evaluation tasks and work independently; active publishing, scientific computing experience, and prior AI evaluation or data-labeling experience are preferred.

Benefits

• Flexible Schedule
• Independent Contractor Work

Listed skills

• Data analysis · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Scientific Computing
• Python
• Research Task Authoring
• Computational Research
• Data Analysis
• Simulation
• Numerical Methods
• Model Fitting
• Data Reduction
• Instrument Data Processing
• Numerical Solvers
• Inverse Problems
• AI Evaluation
• Task Calibration
• Scientific Writing
• Independent Work

Job areas

• Science & Research
• Data & Analytics
• Technology
• Engineering
• Software

More jobs from AfterQuery