Biology & Python Expert - Freelance AI Trainer at Mindrift

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

19 Sep, 26

Salary

35.0

Posted On

21 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Computational Biology, Biophysical Modeling, ODE/PDE Systems, Biomechanical Formulations, Sequence Algorithms, Linux, Problem Design, Technical Writing, English Proficiency

Industry

technology;Information and Internet

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 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. What we look for This opportunity is a good fit for biology specialists with an experience in python open to part-time, non-permanent projects. Ideally, contributors will have: 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+). No prior experience with the listed tools? You're still welcome to apply — as long as you're ready to get up to speed on your own and hit the ground running. How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Project time expectations For this project, tasks are estimated to require around 10–20 hours per week during active phases, based on project requirements. This is an estimate, not a guaranteed workload, and applies only while the project is active. Compensation On this project, contributors can earn up to $35 per hour equivalent, depending on their level and pace of contribution. Compensation varies across projects depending on scope, complexity, and required expertise. Please note that other projects on the platform may offer different earning levels based on their requirements.

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Responsibilities

Design complex computational biology problems and Python reference solutions to challenge and evaluate frontier AI models. Calibrate task difficulty by testing against AI agents to achieve a specific pass rate and refine biophysical models.