Computational Materials Science Researcher (Task Author)

Job Description

Turn computational work from your own materials science research into paid evaluation tasks for AI models. Open to research-track Bachelor's students, Master's and PhD students, postdocs, and recent graduates.

AfterQuery is building a network of researchers to author and validate research-grade scientific-computing tasks for AI models.

A strong task is a real piece of computational work from your own research, something you've actually run and debugged: a phase-field or CALPHAD model, an atomistic simulation, a microstructure or diffraction analysis, or a transport calculation you implemented and validated. Each task has a main problem, a few connected subproblems, a Python reference solution, and tests that check the result. The difficulty should come from the materials science itself, not from withheld information.

We're looking for researchers in: electronic and quantum materials; semiconductors and optoelectronics; metals, alloys, and computational metallurgy; polymers and soft materials; ceramics and glasses; mechanical behavior and structural materials; thermal transport and lattice dynamics; battery and energy materials; surfaces, interfaces, and nanomaterials; and materials informatics.

Requirements

• Education: A Bachelor's student on a research track or with research experience (such as an REU, a thesis, or an RA or lab role), a Master's or PhD student, a postdoc, or a recent graduate in materials science, physics, chemistry, mechanical engineering, or a closely related field.
• Technical: Hands-on experience writing your own code, preferably Python, to model materials or analyze materials data. Running standard packages without your own scripting isn't enough.
• Work authorization: Must be able to work as an independent contractor without visa sponsorship. Students on F-1 visas (including OPT/CPT) are not eligible.

Contract and pay

Flexible, independent-contractor work: $200 per approved task, about 2 hours each, with no cap on how many tasks you take on. Tasks go through review, and you may also review other experts' tasks against a short rubric.

All accepted experts undergo standard background and identity verification.

Why Apply

• 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
• Paid, flexible work: $200 per approved task (about 2 hours each), no cap on how many you take on

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

• Bachelor's student on a research track or with research experience, Master's or PhD student, postdoc, or recent graduate in materials science, physics, chemistry, mechanical engineering, or a closely related field
• Has written their own code, preferably in Python, to model materials or analyze materials data
• Able to turn your own computational research into a well-specified task with a reference solution and tests
• Able to work independently and manage your own time. This is flexible, task-based work with no fixed weekly schedule

Preferred Qualifications

• Research experience in phase-field or CALPHAD modeling, crystal plasticity, phonon transport, or energy materials
• Public code, a thesis, or papers that show your computational work
• Prior experience in data annotation, data labeling, or AI/ML evaluation work

About AfterQuery

AfterQuery is a research lab investigating the boundaries of artificial intelligence through novel datasets and experimentation. We believe great AI comes from exceptional, human-generated data.

We're backed by top investors, including Y Combinator and Box Group, and support all leading AI labs.

Apply today to contribute to pushing the frontier of AI.