Mindrift

Chemistry&PythonExpert-FreelanceAITrainer

Remote PART TIME Remote Friendly
Market Sentiment
HIGH DEMAND

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

The Brief

“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

Industry & Context.

Problems you'll solve

Troubleshooting simulation stalls; Troubleshooting convergence

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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