Mindrift
Chemistry&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Chemistry & Python Expert - Freelance AI Trainer at Mindrift. Skills: Computational chemistry, Python, AI model training. Design computational chemistry problems. Write Python reference solution”
What You'll Achieve.
Achieve 10-30% pass rate; Ensure task quality is high
Industry & Context.
Troubleshoot simulation stalls; Analyze convergence issues
What They're Looking For.
Must Have
2+ years research experience, Python proficiency, Fluency with scriptable computational chemistry package, Ability to design problems, Written English (C1+)
Nice to Have
Experience with GROMACS, Experience with PySCF, Experience with ORCA, Experience with Quantum ESPRESSO, Experience with AutoDock Vina
What You'll Do.
Design computational chemistry problems
Write Python reference solution
Define numerical answer
Test problem against model
Tune problem difficulty
Rewrite molecular geometries
How You'll Work.
Team & Collaboration
Senior reviewer feedback
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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