Mindrift
Biology&PythonExpert-FreelanceAITrainer
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“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.
Design problems requiring specialized tools; Tune problem difficulty for specific success rates; Analyze and understand AI agent behavior and limitations
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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