Experimental Biologist (AI Training)

Overview

We’re looking for experienced bench scientists to help train AI models. You’ll design realistic experimental-biology tasks from your own practice, run them through frontier AI systems, and grade what comes back against the standard you’d hold a colleague to.

The models can already talk fluently about biology. What they can’t yet do reliably is the real work: call a plate QC go/no-go, gate a flow panel, check a cloning strategy, or read a Western blot from the raw images. Your bench judgment becomes the benchmark those models are measured against.

What you’ll actually do

• Design realistic experimental tasks from your own workflows: the scenario, the prompt, and the supporting files an agent would need (plate maps and reader output, gel and blot images, flow summaries, protocols, QC records).
• Run tasks through frontier AI agents and grade the deliverable they produce (a QC summary, experimental plan, or troubleshooting memo) against a professional standard.
• Write clear grading rubrics that specify what a correct deliverable must contain (the right controls, the right wells excluded, the right diagnosis of a failed run), and explain why a response passes or fails.
• Flag concrete failures with evidence: controls ignored, edge effects and outliers missed, wrong normalization, plausible-sounding troubleshooting the raw data contradict, or fabricated values.

Problems draw on whatever you know best

• Molecular and cell biology: cloning, cell culture, and cell-based assays.
• Protein biochemistry: purification, characterization, and assay development.
• Immunology and flow cytometry: panel design and gating.
• Imaging and histology, microbiology and virology.
• In vivo and preclinical work, plus lab operations and analytical sciences.

Roles this fits

Common backgrounds: Research Scientist, Molecular / Cell Biologist, Postdoctoral Fellow.

What we look for

• 3+ years designing and running experiments in a pharma, biotech, or academic research lab (counted after undergraduate study).
• Depth in at least one of: molecular biology and cloning; cell culture and cell-based assays; protein biochemistry; immunology and flow cytometry; imaging and histology; microbiology or virology; in vivo and preclinical work.
• A hands-on practitioner who designs and troubleshoots experiments, not only executes SOPs: you can explain what each control is for and diagnose why a run failed.
• You generate and QC primary data, and can judge from raw instrument output whether a run is usable.
• Master’s or PhD (or current PhD candidate) in biology or a related field, completed in the U.S., Canada, Europe, or the UK.
• Clear written English, comfort with ambiguity, and familiarity with AI/LLM tools like Claude or ChatGPT.
• Based in the United States, Canada, or the UK (Ireland and Australia may also be accepted).

Compensation

Up to $125+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income.

About DataAnnotation

DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.

Apply now

Hourly rate up to $125+ / hour

Commitment Flexible hours

Type Independent contractor

Payouts Weekly

Apply now

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Where you can work

This role is 100% remote, for applicants located in these 5 countries: Australia, Canada, United Kingdom, Ireland and United States.