AI Trainer (Life Sciences)

Micro1; India; Remote; INR 7,768,000 - 15,537,000; Full time

Job summary

micro1 is seeking a Computational Life Scientist for a flexible, contractor role. This fully remote position requires a commitment of 15+ hours per week to create AI-training tasks, evaluate model outputs, and define success criteria for scientific tasks.

You will design realistic tasks, establish reproducible environments, and review AI-generated code and analyses. Strong Python skills and deep expertise in a life-science field are essential, with opportunity to contribute to cutting-edge AI

Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Deep expertise in at least one life-science specialization.
• Hands-on experience with computational methods for modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.

Responsibilities

• Create realistic computational life-science tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated code, analyses, models, experiments, and technical deliverables.
• Identify invalid biological assumptions, methodological errors, data leakage, statistical issues, or reproducibility failures.

Skills

Python

Bioinformatics

Statistics

Tools

NumPy

SciPy

Pandas

Biopython

PyTorch

JAX

Job description

Job DescriptionPay: $60–$120/hour equivalent, paid per accepted taskLocation: Global, fully remoteJob Type: ContractorTime Commitment: 15+ hours per weekSchedule: Flexiblemicro1 is looking for Computational Life Scientists with prior professional AI-training or model-evaluation experience to support a project focused on improving advanced AI systems.You will use your scientific expertise and programming skills to create challenging technical tasks, evaluate model-generated code and analyses, identify failures, and establish objective standards for correct solutions. This role is intended for candidates who have completed substantive AI-training work involving life-science or biomedical content.

What You’ll Work On

• Create realistic computational life-science tasks that challenge advanced AI models.
• Develop task instructions, reproducible environments, reference solutions, and evaluation criteria.
• Evaluate model-generated code, analyses, models, experiments, and technical deliverables.
• Identify invalid biological assumptions, methodological errors, data leakage, statistical issues, or reproducibility failures.

Required Qualifications

• Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
• Strong practical proficiency with Python.
• Deep expertise in at least one relevant life-science specialization.
• Hands‑on experience using computational methods for modeling, simulation, bioinformatics, biomedical analysis, statistics, or reproducible research.
• Relevant specializations may include biology, computational biology, bioinformatics, medicine and health, biomedical science, neuroscience, genomics, ecology, and evolution.

Public Proof of Skill

Strong applicants should provide evidence such as: Peer-reviewed publications or preprints involving computational methods Public research code or bioinformatics pipelines Google Scholar, ORCID, or equivalent research profiles PhysioNet or other biomedical competition results Open‑source scientific software contributions Public datasets, models, notebooks, or technical projects

Preferred Qualifications

Experience creating AI‑training tasks, reference answers, rubrics, graders, or benchmark problems. Experience reviewing other AI trainers’ technical submissions. Experience with NumPy, SciPy, pandas, Biopython, PyTorch, JAX, or comparable scientific tools. Prior experience with long‑horizon or agentic AI evaluations. Availability to begin immediately.

Compensation

Compensation is output‑based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.