Mindrift

Biology&PythonExpert-FreelanceAITrainer

Remote PART TIME Remote Friendly
Market Sentiment
HIGH DEMAND

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

The Brief

“Biology & Python Expert - Freelance AI Trainer at Mindrift. Skills: Computational biology, Python, AI model testing. Design computational biology problems. Write Python reference solution”

What You'll Achieve.

Design task scoring within range; Achieve 10-30% pass rate

Industry & Context.

Problems you'll solve

Problem design; Problem tuning; Troubleshooting simulation

Eligibility Requirements

Linux container environment

What They're Looking For.

Must Have

2+ years research experience, 2+ years applied experience, 2+ years teaching experience, Python proficiency, written English (C1+)

Nice to Have

Degree in Biology, Fluency with NEURON, Fluency with Brian2, Fluency with NEST, Fluency with OpenSim, Fluency with AMICI, Fluency with libroadrunner, Fluency with MNE-Python, Fluency with others, Willingness to learn computational biology package, Ability to design problems, Willingness to independently learn package

What You'll Do.

Design computational biology problems

Write Python reference solution

Define model definitions

Define network definitions

Decide numerical answer

Test problem against model

Tune problem difficulty

Rewrite channel kinetics

Tighten stimulation protocols

Tighten solver tolerances

Observe agent behavior

Analyze simulation stalls

Analyze model convergence

How You'll Work.

Communication Scope

Written English

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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