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. Design problems hinging on biophysical models”
What You'll Achieve.
Achieve 10-30% pass rate; Ensure task quality is high
Industry & Context.
Problem design; Agent tuning; Solver tolerance tuning
Linux container environment
What They're Looking For.
Must Have
2+ years research experience, 2+ years applied experience, 2+ years teaching experience, Python proficiency, Degree in Biology, Degree in related field, written English C1+
Nice to Have
Fluency with NEURON, Fluency with Brian2, Fluency with NEST, Fluency with OpenSim, Fluency with AMICI, Fluency with libroadrunner, Fluency with MNE-Python, Fluency with other scriptable computational biology package, Willingness to learn NEURON, Willingness to learn Brian2, Willingness to learn NEST, Willingness to learn OpenSim, Willingness to learn AMICI, Willingness to learn libroadrunner, Willingness to learn MNE-Python, Willingness to learn other scriptable computational biology package, Independent learning of tools
What You'll Do.
Design computational biology problems
Design problems hinging on biophysical models
Design problems hinging on ODE systems
Design problems hinging on PDE systems
Design problems hinging on biomechanical formulations
Design problems hinging on sequence algorithms
Write Python reference solution
Supply model definitions
Supply network definitions
Decide numerical answer
Define answer tolerance
Test problem against model
Tune problem difficulty
Rewrite channel kinetics
Tighten stimulation protocols
Tighten solver tolerances
Observe agent behavior
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 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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