About this role
Overview
We’re looking for experienced drug discovery scientists to help train AI models. You’ll design realistic discovery and preclinical tasks from your own practice, run them through frontier AI systems, and grade what comes back against a professional standard.
The models can talk fluently about drug discovery. What they can’t yet do reliably is the real work: SAR analysis, screening triage, DMPK and PK/PD interpretation, or a candidate-selection call. Your judgment moving programs from target to IND becomes the benchmark those models are measured against.
What you’ll actually do
- Design realistic discovery tasks from your own workflows: the scenario, the prompt, and the files an agent would need (assay result tables, SAR spreadsheets, DMPK summaries, study reports, program-review decks).
- Run tasks through frontier AI agents and grade the deliverable (a decision memo, screening workbook, or tox summary) against the standard you’d hold a colleague to.
- Write clear grading rubrics (the right compounds advanced, the right liabilities flagged, the right calculations) and explain why a response passes or fails.
- Flag concrete failures with evidence: misread assay data, SAR conclusions the data don’t support, PK parameters misinterpreted, unit and scaling errors, or missed safety liabilities.
Problems draw on whatever you know best:
- Medicinal chemistry and SAR / lead optimization.
- Computational chemistry and CADD.
- Assay development and screening.
- DMPK, PK/PD, ADME, and preclinical safety.
- Translational and biomarker science, biologics and advanced modalities, and CMC / process development.
Roles this fits
Common backgrounds: Medicinal Chemist, DMPK / PK-PD Scientist, Discovery Biologist.
What we look for
- 3+ years hands-on in a discovery or preclinical setting (pharma, biotech, or an academic drug-discovery unit), counted after undergraduate study.
- Depth in at least one of: medicinal chemistry and SAR; computational chemistry / CADD; assay development and screening; DMPK / PK-PD / ADME; preclinical safety and toxicology; translational and biomarker science; protein or antibody engineering; cell and gene therapy; CMC and formulation.
- Familiarity with drug discovery end to end: target validation, hit finding, hit-to-lead, lead optimization, candidate selection, and IND-enabling studies.
- A hands-on practitioner who does (or recently did) the bench or analysis work yourself, not solely in a managerial capacity.
- Master’s or PhD (or current PhD candidate) in biology, chemistry, pharmacology, 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).
How it works
Apply
Qualify
Work & get paid
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.
