Mindrift

MaterialsEngineer&PythonExpert-FreelanceAITrainer

Remote PART TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“Materials Engineer & Python Expert - Freelance AI Trainer at Mindrift. Skills: Material Science, Python, AI training. Design computational material science problems. Write Python reference solution”

What You'll Achieve.

Achieve 10-30% pass rate

Industry & Context.

Problems you'll solve

Troubleshooting simulations; Troubleshooting inversions; Troubleshooting flow models

What They're Looking For.

Must Have

Degree in Material Science, 2+ years research experience, 2+ years applied experience, 2+ years teaching experience, Python proficiency, Written English (C1+)

Nice to Have

Experience with ObsPy, Experience with instaseis, Experience with pyrocko, Experience with MITgcm, Experience with xmitgcm, Experience with flopy / MODFLOW, Willingness to learn scriptable package

What You'll Do.

Design computational material science problems

Write Python reference solution

Supply model definitions

Supply domain definitions

Decide numerical answer

Test problem against model

Tune problem difficulty

Rewrite waveform scenarios

Tighten inversion parameters

Tighten solver tolerances

How You'll Work.

Team & Collaboration

Senior reviewer feedback

Communication Scope

Written English

Full Job Description

_**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 isproject-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. Generic data wrangling around synthesised toy data won't cut it. 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. They will provide 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. Reaching that means rewriting waveform scenarios, tightening inversion parameters and solver tolerances, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where a flow or inversion model converges. This time compounds in two direction

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