Position: AI Trainer (Life Sciences)
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
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 methodsPublic research code or bioinformatics pipelinesGoogle Scholar, ORCID, or equivalent research profilesPhysio Net or other biomedical competition resultsOpen-source scientific software contributionsPublic datasets, models, notebooks, or technical projectsPreferred Qualifications
Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.Experience reviewing other AI trainers’ technical submissions.Experience with Num Py, Sci Py, 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.