Biology & Life Sciences Data Annotator
A Biology & Life Sciences Data Annotator helps train and evaluate AI by applying expert biology, biotechnology and life sciences knowledge to structured data, model outputs and quality review workflows.
Annotate biological concepts, mechanisms, pathways, organisms and experimental findings for text and multimodal AI datasets.
Key responsibilities
- Map concepts to project ontologies.
- Label causal and functional relationships.
- Validate terminology and context.
- Identify ambiguity requiring domain escalation.
What we look for
- Biology, Biotechnology, Biomedical Science or related degree.
How success is measured
Ontology consistency, annotation accuracy.
How this works on Jwuma
Candidate profile → skills evidence → domain assessment → calibration task → qualification → project matching → production → peer review → expert QA → performance feedback and progression. Corpshore AI service alignment Applicable across annotation and labeling, RLHF and preference data, model evaluation and red-teaming, multimodal datasets and specialized AI data operations, depending on project scope.
Common questions
What does a Biology & Life Sciences Data Annotator do?
A Biology & Life Sciences Data Annotator helps train and evaluate AI by applying expert biology, biotechnology and life sciences knowledge to structured data, model outputs and quality review workflows.
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