Mindrift
Biology&PythonExpert-FreelanceAITrainer
Neural analysis suggests this role is
optimal for Mid+ candidates.
“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.
Problem design; Problem tuning; Troubleshooting simulation
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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