Materials Scientist & Python AI Problem Designer

Please submit your CV in English and indicate your level of English proficiency. Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.

What this opportunity involves:

• You design computational material science problems to challenge a frontier AI model. The problem must have an answer verifiable by code, and the problem has to require a specialized tool like ObsPy, instaseis, pyrocko, MITgcm, flopy/MODFLOW, or others.
• Each problem runs inside a sealed Linux container with the tool pre-installed and a programmatic judge that grades the model's answer.
• As an expert author, you:
• Pick an anchor tool and design a problem that hinges on its waveform-processing kernels, geophysical inversion routines, sub-surface flow solvers, or community-validated data pipelines.
• Write a Python reference solution, supply input files and model or domain definitions where needed.
• Decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right.
• Test the problem against the model in batches of parallel attempts, tuning the problem difficulty until the agent only succeeds in a small number of attempts.
• Once you're happy with the task, and it scores within range, the task goes to a senior reviewer in your subfield for feedback to ensure task quality is high.
• Calibration requires patience. You're tuning the problem against batches of parallel runs of the agent, aiming for a pass rate in the 10-30% band.
• This time compounds in two directions. You come out of each task with deeper command of the anchor tool itself, and also get a hands-on working intuition for how a frontier model navigates complex seismic, oceanographic, and sub-surface flow problems.

What we look for:

• This opportunity is a good fit for material scientists & engineers with experience in Python open to part-time, non-permanent projects. Ideally, contributors will have:
• Degree in Material Science or related field;
• 2+ years of research, applied, or teaching experience;
• Python proficiency for writing reference solutions;
• Fluency with — or strong willingness to independently learn — at least one scriptable package: ObsPy, instaseis, pyrocko, MITgcm, xmitgcm, flopy / MODFLOW, or GeoPandas;
• Ability to design problems that genuinely require a specialized solver;
• Strong written English (C1+).
• No prior experience with the listed tools? You're still welcome to apply — as long as you're ready to get up to speed on your own and hit the ground running.

How it works:

• Apply - Pass qualification(s) - Join a project - Complete tasks - Get paid

Project time expectations:

• For this project, tasks are estimated to require around 10-20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active.

Compensation:

• On this project, contributors can earn up to $35 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise.