AI Trainer (Life Sciences)

Overview

micro1 is seeking Computational Life Scientists with prior professional AI-training or model-evaluation experience to help improve advanced AI systems. The work is fully remote and paid on a per-task basis, with compensation tied to accepted outputs.

Successful candidates should bring scientific depth, practical programming ability, and experience working on technical AI tasks in life-science or biomedical settings.

What You'll Work On

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

• 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 similar scientific tools.
• Prior experience with long-horizon or agentic AI evaluations.
• Availability to begin immediately.

Application Process

1. Submit your application, AI-training experience, specialization, and public technical evidence.
2. Complete an AI interview with technical and integrity checks.
3. Complete a hiring-manager review.
4. If selected, complete project onboarding and begin your first assigned task.

Compensation

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