Mindrift

Biology&PythonExpert-FreelanceAITrainer

South Africa PART TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Entry candidates.

The Brief

“Biology & Python Expert - Freelance AI Trainer at Mindrift. Skills: Biology expertise, Python programming, Computational biology tools. Design computational biology problems to challenge AI models. Write Python reference solutions”

What You'll Achieve.

AI agent succeeds in a small number of attempts (10-30% pass rate); Task quality is high

Industry & Context.

Problems you'll solve

Design problems requiring specialized tools; Tune problem difficulty for specific success rates; Analyze and understand AI agent behavior and limitations

Eligibility Requirements

Work within a sealed Linux container, Problems have programmatic judges

What They're Looking For.

Must Have

Degree in Biology 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 computational biology package: NEURON, Brian2, NEST, OpenSim, AMICI, libroadrunner, MNE-Python, or

What You'll Do.

Design computational biology problems to challenge AI models

Write Python reference solutions

Supply input files and model/network definitions

Determine numerical answers and tolerances

Test problems against AI models

Tune problem difficulty

Rewrite channel kinetics

tightening stimulation protocols and solver tolerances

Analyze AI agent behavior and simulation performance

How You'll Work.

Team & Collaboration

Submit tasks to senior reviewers for feedback

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 is project-based, not permanent employment.** **What this opportunity involves** You design computational biology 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 NEURON, Brian2, OpenSim, AMICI, MNE-Python, or others. Generic data wrangling around a genome browser 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 biophysical models, ODE/PDE systems, biomechanical formulations, or sequence algorithms. * Write a Python reference solution, supply input files and model or network 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 channel kinetics, tightening stimulation protocols and solver tolerances, and watching how the agents act. You'll learn how these agents cut corners, where a simulation stalls, where a neural or biomechanical model converges. This time compounds in two directions. You come out of each task with deeper command

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