Digital Pathology Annotator
We’re looking for a detail-oriented Digital Pathology Annotator to support ongoing data generation for AI model development. This role focuses on high-quality annotation of histopathology images (H&E and IHC) across multiple oncology indications.
Your work will directly impact model performance, making precision, consistency, and attention to detail critical. This is a hands-on role centered on structured annotation tasks and adherence to defined protocols.
Responsibilities
Annotate whole slide images (H&E, IHC) using tools such as QuPath
Identify and label tumor regions, stromal components, immune cells, and relevant biomarkers
Follow strict annotation protocols and class definitions
Ensure consistency and accuracy across large volumes of data
Maintain high-quality outputs aligned with model training requirements
Position terms (review before applying):
Location: Tel Aviv (close to train and light rail)
Employment type: Part-time, flexible capacity (ideal for students), hourly contract
Background in pathology, histology, or a related biomedical field
Hands-on experience with digital pathology tools (QuPath preferred)
Strong understanding of tissue morphology and staining patterns (H&E, IHC)
High attention to detail and ability to follow structured guidelines
Ability to work independently and meet defined quality thresholds
