AI Trainer (Life Sciences)

micro1 Inc.; United States; Remote; USD 83,000 - 165,000; Part time

Job summary

micro1 is seeking Computational Life Scientists to support a project aimed at improving advanced AI systems. The role involves using your scientific and programming skills to craft challenging tasks, evaluate model outputs, and establish objective standards for correct solutions.

This position targets candidates with substantive AI-training work in life-science or biomedical content. Responsibilities include creating realistic life-science tasks, defining instructions and evaluation criteria,

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.

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 proficiency

AI training/evaluation experience

Life-science specialization

Reproducible research practices

Tools

NumPy

SciPy

Pandas

Biopython

PyTorch

JAX

Job description

Pay: $60–$120/hour equivalent, paid per accepted task

Location: Global, fully remote

Job Type: Contractor

Time Commitment: 15+ hours per week

Schedule: Flexible

micro1 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

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

Required Qualifications

1. Prior paid experience training, evaluating, benchmarking, or improving AI systems on scientific or technical tasks.
2. Strong practical proficiency with Python.
3. Deep expertise in at least one relevant life-science specialization.
4. 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:

1. Peer-reviewed publications or preprints involving computational methods
2. Public research code or bioinformatics pipelines
3. Google Scholar, ORCID, or equivalent research profiles
4. PhysioNet or other biomedical competition results
5. Open-source scientific software contributions
6. Public datasets, models, notebooks, or technical projects

Preferred Qualifications

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

Compensation

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