Scientific Computing Research Expert

Scientific Computing Research Expert

$134,400–$153,600/year estimated full-time equivalent $70–$80/hour listed rate $300 per approved task | Approximately 4 hours per task Remote | Independent Contractor Estimated full-time equivalent is based on the listed hourly rate across 40 hours/week and 48 working weeks. This is not guaranteed annual compensation. Actual earnings are task-based and depend on the number of approved tasks you complete. About The Role We’re seeking PhD-level and research-active scientific computing experts to author and calibrate research-grade evaluation tasks for AI models.

The strongest tasks are based on real computational work from your own research—work you have actually run, debugged, analyzed, or used professionally. This could include data reduction, model fitting, simulation setup, instrument data processing, numerical solvers, inverse problems, or other computational research workflows.

The difficulty should come from the underlying science and computational reasoning rather than artificially withheld information.

What You’ll Do

• Design and author original, research-grade AI evaluation tasks based on your own computational research.
• Build tasks around authentic scientific workflows such as data analysis, model fitting, simulation, numerical methods, or computational problem-solving.
• Define the metrics and characteristics that should be used to judge a strong solution without publishing the exact passing thresholds.
• Run and debug your own computational work to ensure tasks are technically sound and reproducible.
• Revise submitted tasks based on structured review feedback until they meet approval requirements.
• Optionally review and calibrate tasks submitted by other experts in your field.

Research Areas

Tasks Are Commissioned Across Multiple Scientific Disciplines, Including

• Life sciences: biology, ecology, medicine, neuroscience
• Physical sciences: astronomy, chemistry, materials science, physics
• Earth sciences: atmospheric, environmental, geosciences, ocean sciences

Required Qualifications

• Currently pursuing or holding a PhD with active, hands-on research experience.
• A Master’s degree may qualify if you are research-active through thesis-based enrollment, an active research assistant or laboratory role, a real publication, or a clear PhD trajectory.
• Working proficiency in Python, sufficient to run and debug your own computational work.
• Active hands-on research or technical experience in your field.
• Ability to transform your own computational research into a clearly defined, research-grade evaluation task.
• Strong independent working and time-management skills.

A Master’s followed by a purely industry role with no active research component is not considered a fit for this opportunity.

Preferred Qualifications

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

Work & Compensation

• Remote, independent-contractor engagement
• $300 per approved task
• Each task requires approximately 4 hours of work, corresponding to an effective rate of approximately $75/hour at the stated estimate.
• Tasks are reviewed before approval and commonly require one or two rounds of revision.
• Specific feedback is provided, and revisions can be resubmitted.
• No fixed weekly schedule
• No cap on the number of tasks you can take on.
• Work can be completed according to your own schedule.
• Standard background and identity verification is required for accepted experts.

Why This Opportunity

Turn genuine computational research experience into high-quality evaluation tasks that test AI models against real scientific reasoning and computational work . This is an opportunity to contribute directly to how advanced AI systems are evaluated across a broad range of scientific disciplines while maintaining a flexible, task-based schedule.