Mindrift
MaterialsEngineer&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Materials Engineer & Python Expert - Freelance AI Trainer at Mindrift. Skills: Materials science, Python, AI training. Design computational material science problems. Write a Python reference solution”
What You'll Achieve.
Succeeds in small number of attempts; Task scores within range; Task quality is high; Pass rate in 10–30% band
Industry & Context.
Design problems; Troubleshooting
English proficiency
What They're Looking For.
Must Have
2+ years of research, Python proficiency for writing reference, Fluency with ObsPy, Fluency with instaseis, Fluency with pyrocko, Fluency with MITgcm, Fluency with xmitgcm, Fluency with flopy / MODFLOW, Fluency with written English (C1+), Willingness to learn scriptable package, Ability to design problems, Ready to get up to speed
Nice to Have
Degree in Material Science, Experience in python, Willingness to independently learn package
What You'll Do.
Design computational material science problems
Write a Python reference solution
Supply model definitions
Supply domain definitions
Decide the numerical answer
Test the problem against the model
Tune the 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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