AI Trainer (Life Sciences)
micro1
At a glance
AI Trainer (Life Sciences) is a remote other opening at micro1. The posting places it in Global, fully remote. The post date on the listing is 9 Nov 2026. No deadline is filled in on the listing. The notes below are written by Akazi. The advertisement under them is the employer's own text.
- Company
- micro1
- Job type
- Other · Remote
- Location
- Global, fully remote
- Deadline
- Deadline not listed
- Date posted
- 9 Nov 2026
How to apply
- AI Trainer (Life Sciences) is listed here so you can find it. micro1 takes the application. Akazi does not.
- Use Apply. It opens the employer's own site. Finish every step there, including an account if they ask for one.
- No deadline is stored in the listing fields. Read the advert in case the date is only written in the text, and apply while it is still open.
- It is remote (Global, fully remote). Say that you are based in Rwanda, which hours you can overlap with the team, and which languages you work in.
- Shape your CV around this other role and put the experience they ask for near the top. Name Kinyarwanda, English, or French only at the level you can actually work in.
- Do not pay a fee, buy a starter kit, or share a mobile-money PIN. A real application to micro1 will not ask you to pay in order to be hired.
Writing a CV for Rwanda · Preparing for interviews · Remote work from Rwanda
Apply(opens in a new tab)Employer advertisement
The text below is the public posting from micro1. Akazi did not write it.
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 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.
Application Process
Submit your application, AI-training experience, specialization, and public technical evidence. Complete an AI interview containing technical and integrity checks. Complete a hiring-manager review. If selected, complete project onboarding and begin your first assigned task.
Compensation
Compensation is output-based. Experts are paid for tasks that meet project specifications and quality requirements. Task availability and completion time may vary.
View the original posting(opens in a new tab)
Similar openings
Other live roles at micro1, or in the same kind of work, if Akazi has them.