Mindrift
Chemistry&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Chemistry & Python Expert - Freelance AI Trainer at Mindrift. Skills: Chemistry, Python, AI Training. Design computational chemistry problems. Write a Python reference solution”
What You'll Achieve.
scores within range; pass rate in the 10–30% band
Industry & Context.
design a problem that hinges on its; tuning the problem against batches; tuning the problem difficulty
CV in English, Linux container
What They're Looking For.
Must Have
Chemistry specialists, Python proficiency, written English (C1+)
Nice to Have
scriptable computational chemistry package, design problems that genuinely require a specialized
What You'll Do.
Design computational chemistry problems
Write a Python reference solution
Decide the numerical answer
Test the problem against the model
Tune the problem difficulty
Rewrite molecular geometries
How You'll Work.
Team & Collaboration
senior reviewer in your subfield; provide feedback to ensure task quality
Communication Scope
English proficiency
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 is project-based, not permanent employment.** **What this opportunity involves** You design computational chemistry 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 GROMACS, PySCF, ORCA, Quantum ESPRESSO, AutoDock Vina, or others. RDKit-style cheminformatics dressed up around generic libraries 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 force fields, integrators, electronic-structure methods, basis sets, or pseudopotential workflows. * Write a Python reference solution, supply input files and molecular or crystal structure 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 molecular geometries, tightening basis sets and convergence thresholds, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where a self-consistent field cycle converges. This time compounds in two directio
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