AI Trainer (Life Sciences)

$60–120/hr

rate as listed

Workload: 15+ hrs/week
Engagement: Project-based
Location: Worldwide
Field: Generalist

Create computational life-science tasks and evaluate AI-generated code, analyses, models, and experiments for scientific validity and reproducibility. This is a global remote contractor role requiring at least 15 hours per week on a flexible, task-based schedule.

The work

You will design realistic computational life-science problems, write precise instructions, build reproducible environments, produce reference solutions, and define objective evaluation criteria. Day-to-day review work includes running model-generated code, checking analyses and technical deliverables, and identifying biological errors, weak methodology, data leakage, statistical problems, and reproducibility failures.

What micro1 screens for

The AI interview includes technical and integrity checks, with emphasis on prior paid AI-training or model-evaluation work involving scientific tasks. Candidates should be ready to discuss a life-science specialization in depth, explain specific evaluation decisions under follow-up, and point to public technical evidence such as publications, research profiles, code, pipelines, competition results, datasets, models, or notebooks. Strong practical proficiency with Python is required. Experience with task authoring, rubrics, graders, submission review, scientific computing tools, or long-horizon and agentic evaluations is preferred.

Logistics

The role is open globally and is fully remote, with a contractor arrangement and a commitment of 15+ hours per week. The schedule is flexible and task availability may vary; the listing does not explicitly promise fully asynchronous work. Compensation is output-based and depends on tasks meeting project specifications and quality requirements, and availability to begin immediately is preferred.

Pay band: $60–120/hr

What they look for

Biology
Mathematics
Ecology
Materials Sciences
Earth
Climate and Oceans
Statistics
AI Training
Programming Skills

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether your prior AI work was substantive and paid, and whether you can separate your own evaluation responsibilities from the broader project.
A strong answer: Name the project context, scientific domain, models or outputs evaluated, tasks you personally completed, quality process, and a concrete failure pattern or improvement you observed. State what evidence can verify the work while respecting confidentiality.
Where people lose it: Candidates lose points by describing ordinary data analysis or model development without showing that they evaluated or improved an AI system.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.