Mindrift
AI
MaterialsEngineer&PythonExpert-FreelanceAITrainer
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“Materials Engineer & Python Expert - Freelance AI Trainer at Mindrift. Skills: Materials Science, Python, AI Training. Design computational material science problems to challenge a frontier AI model. Write a Python reference solution”
What You'll Achieve.
Design tasks that score within range; Ensure task quality is high
Industry & Context.
Design problems that genuinely require a specialized tool; Tune problem difficulty until agent succeeds in a small number of attempts; Achieve a pass rate in the 10–30% band
Problems run inside a sealed Linux container, Tool pre-installed in container, Programmatic judge grades model's answer
What They're Looking For.
Must Have
Degree in Material Science or related, 2+ years of research, applied, or teaching, Python proficiency for writing reference, Ability to design problems that genuinely require a specialized, written English (C1+)
Nice to Have
Fluency with — or willingness to independently learn — at least one scriptable package: ObsPy, instaseis, pyrocko, MITgcm, xmitgcm, flopy / MODFLOW
What You'll Do.
Design computational material science problems to challenge a frontier AI model
Write a Python reference solution
Supply input files and model or domain definitions
Decide the numerical answer and tolerance
Test the problem against the model
Tune problem difficulty
Rewrite waveform scenarios
Tighten inversion parameters and solver tolerances
Analyze simulation stalls
Analyze flow or inversion model convergence
How You'll Work.
Team & Collaboration
Submit tasks to senior reviewers for feedback; Incorporate feedback from senior reviewers
Communication Scope
Written English (C1+)
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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