Biology & Python Expert - Freelance AI Trainer

Mindrift

About The Position

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. 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. You write a Python reference solution, supply input files and model or network definitions where needed. You decide the numerical answer and how close the model needs to get — with a domain-appropriate tolerance — to count as right. You 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 of the anchor tool itself, and also get a hands-on working intuition for how a frontier model navigates complex neuronal, biomechanical, and systems biology problems.

Requirements

Biology specialists with experience in Python.
Open to part-time, non-permanent projects.
Degree in Biology or related field.
2+ years of research, applied, or teaching experience.
Python proficiency for writing reference solutions.
Fluency with — or strong willingness to independently learn — at least one scriptable computational biology package: NEURON, Brian2, NEST, OpenSim, AMICI, libroadrunner, MNE-Python, or Biopython.
Ability to design problems that genuinely require a specialized solver.
Strong written English (C1+).
Readiness to learn tools independently and hit the ground running.

Responsibilities

Design computational biology problems to challenge a frontier AI model.
Ensure problems have an answer verifiable by code and require a specialized tool.
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.
Decide the numerical answer and the required tolerance for the model's answer.
Test the problem against the model in batches of parallel attempts, tuning difficulty.
Submit tasks to a senior reviewer for feedback.
Tune the problem against batches of parallel runs of the agent, aiming for a 10-30% pass rate.