Scientific Computing Research Expert

Contract · Remote

Hourly Rate: $70–$80/hr
Task-Based Compensation: $300 per approved task

The hourly rate is provided as the stated engagement rate. Compensation is primarily task-based, with each approved task paying $300. Each task typically requires approximately 4 hours of work. There is no fixed weekly schedule or cap on the number of tasks you can complete.

About the Role

We’re building a network of research-active scientific computing experts to author and calibrate research-grade evaluation tasks for AI models.

The work is based on genuine computational research rather than artificial or theoretical exercises. A strong task may involve data reduction, model fitting, simulation setup, instrument data processing, numerical solvers, inverse problems, or another computational workflow you have actually performed, debugged, or used in your research or technical work.

The goal is to create challenging evaluation tasks where the difficulty comes from the underlying scientific problem and requires genuine domain expertise.

Scientific Disciplines

Tasks may come from a broad range of scientific fields, including:

• Mathematical sciences — applied mathematics, formal mathematics, operations research, statistics

• Engineering sciences — chemical, civil, electrical, mechanical engineering

What You’ll Do

• Design and author original research-grade evaluation tasks based on your own computational research or technical work.

• Turn real computational workflows into clearly specified tasks that can be evaluated consistently.

• Define the metrics and criteria that should be used to assess strong solutions without publishing exact passing thresholds.

• Run and debug your own computational work to ensure tasks are technically sound.

• Revise submitted tasks based on structured review feedback.

• Respond to reviewer feedback and make revisions until tasks meet the required quality standard.

• Optionally review and calibrate tasks submitted by other experts in your scientific domain.

What We’re Looking For

• Currently pursuing or holding a PhD with active hands-on research experience.

• Master’s candidates or graduates may qualify if they have a research-active background, such as:

  • Thesis-based enrollment

  • Active research assistant or laboratory role

  • Peer-reviewed publication or other substantive research output

  • Clear trajectory toward a PhD

• Working proficiency in Python sufficient to run, modify, troubleshoot, and debug your own computational work.

• Ability to translate genuine research or technical workflows into well-specified evaluation tasks.

• Strong understanding of the scientific methods and computational techniques relevant to your field.

• Ability to work independently and manage your own time.

• Strong written communication and attention to technical detail.

A Master’s followed by a purely industry-based role with no continuing research component is generally not a fit for this engagement.

Preferred Experience

• Current PhD student, postdoctoral researcher, or faculty member actively conducting or publishing research.

• Scientific computing or computational research experience.

• Data analysis, numerical methods, simulation, modeling, or scientific programming.

• Experience processing experimental or instrument-generated data.

• Experience with data annotation, data labeling, AI evaluation, or machine learning evaluation.

Engagement & Compensation

• Remote independent-contractor work

• Flexible, task-based schedule

• $300 per approved task

• Each task typically requires approximately 4 hours

• No fixed weekly commitment

• No stated cap on the number of tasks you can complete

• Tasks are reviewed before approval

• Most tasks require one or two rounds of revision before approval

• Specific feedback is provided during the review process

• Approved tasks can be resubmitted after revisions

• Standard background and identity verification is required

This role is designed for researchers who want to apply their existing scientific and computational expertise to the evaluation of advanced AI systems without committing to a fixed work schedule