Life Sciences Expert, AI Trainer

Frontier models can explain the biology on paper and still make the wrong call on real data. This role puts your judgment to work finding out where. You turn problems from your own work into realistic tasks, run them through frontier models, and grade the answers the way you would review a colleague's work.

We are looking for people in three tracks:

Experimental biology. Bench scientists who design and troubleshoot their own experiments. Think of a QC call on a cell-based assay, a flow panel gating review, or reading a Western blot from the raw images.

Drug discovery. Scientists who move programs from target to candidate in pharma, biotech, or an academic discovery unit. Think of a lead optimization decision, triaging a screening campaign, or summarizing a preclinical safety package.

Computational biology and bioinformatics. Analysts who take biological data from raw files to a final interpretation. Think of QC and interpretation of an RNA-seq dataset, a variant prioritization report, or auditing an analysis pipeline.

You set your own schedule, and clear written reasoning counts as much as scientific depth.

Key responsibilities

• Build realistic tasks from problems in your own life sciences work
• Give those tasks to frontier AI models and judge the results against the standard of your field
• Weigh paired model answers against each other and explain which one holds up
• Write grading rubrics for your tasks and set out your reasoning in writing

Ideal qualifications

• 2+ years of hands-on experience in a pharma or biotech lab, an academic lab, or a research institute, counted after your undergraduate degree
• Degree in biology or a closely related field, completed or in progress, earned in the US, Canada, Europe, or the UK
• Experimental biology: you design and troubleshoot experiments yourself and can judge raw instrument output, not only run established protocols
• Drug discovery: hands-on discovery or preclinical work; degrees in chemistry, medicinal chemistry, pharmacology, pharmaceutical sciences, or toxicology also count
• Computational biology: you write and can explain your own analysis code in Python or R; biostatistics, computer science, or statistics degrees count when paired with real biological data work
• Full professional or native-level written English
• Based in the United States, United Kingdom, Ireland, Canada, or Australia

Requirements

• Based in the United States, United Kingdom, Ireland, Canada, or Australia
• Bachelor's degree or higher in biology or a closely related field, completed or in progress, earned in the US, Canada, Europe, or the UK
• 2+ years of hands-on lab, drug discovery, or biological data experience preferred
• Available at least 10 hours per week
• PayPal account for payment
• Willing to work without LLM assistance where the project does not permit it
• Use one email address to sign up, take the assessment, and get paid

Nice to have

• Programming experience, for example in Python or R
• Have used Claude or ChatGPT in your professional work

What success looks like

• You clear the skills assessment on your own work, with no AI assistance
• Your tasks come from real problems in your own practice, and a frontier model genuinely fails them
• Your rubrics are specific enough that another expert would grade the same response the same way
• You act on reviewer feedback without needing to be walked through it
• You keep at least 10 hours a week going, week after week

Payment & terms

• $40 to $125 USD an hour. The rate varies with your expertise, education, and experience. Minimum 10 hours a week with no weekly maximum; full-time hours are available. Independent contractor, fully remote. Payment runs through PayPal, so you need a PayPal account. As an independent contractor, you handle your own taxes. Getting on: a skills assessment that takes most people four to six hours. You are paid $240 for it if you pass, and you get one attempt, so take your time and do your best work. Work produced with AI tools is rejected. After you pass: onboarding and a short training project, then you propose a task from your own practice, build it, and run it against frontier models. Each stage is reviewed before you move on, and you are paid for the work you complete.