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Annotator at Imagene AI

Tel Aviv, Tel-Aviv District, Israel -

Full Time

Start Date

Immediate

Expiry Date

23 May, 26

Salary

9000.0

Posted On

22 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Detail-Oriented, Methodical, Consistency, Quality Control, Data Validation, Guideline Adherence, Repetitive Task Management, Clinical Information Review

Industry

Medical and Diagnostic Laboratories

Description

We’re looking for a detail-oriented Annotator to support AI models that are reshaping how cancer patients receive treatment. This role focuses on reviewing and validating manually annotated data used to train large language models (LLMs). Your work will directly impact the accuracy and reliability of our machine learning systems. This is a foundational role focused on high-precision annotation and quality control. Over time, there may be opportunities to take on broader responsibilities, including evaluating model outputs, but the core of the role remains hands-on quality assurance work. Responsibilities Review and validate annotated data prepared by external contractors. Examine Kaplan–Meier graphs and accurately mark specific clinical information according to defined guidelines. Ensure consistency, accuracy, and adherence to quality standards across large volumes of data. Identify issues in annotations and flag them appropriately to maintain high data quality. Requirements Extremely detail-oriented and methodical. Comfortable performing repetitive, structured tasks with focus and consistency over extended periods. Able to follow precise guidelines and apply them consistently. Responsible, reliable, and quality-driven. Position terms (review before applying): Location: Tel Aviv (close to train and light rail) Work model: 4 days per week in the Tel Aviv office + 1 fixed work-from-home day Employment type: Full-time, global contract Compensation: 9,000 NIS per month + 1,000 NIS monthly Cibus (food allowance)

Responsibilities

The Annotator will review and validate manually annotated data, specifically examining Kaplan–Meier graphs and accurately marking clinical information according to defined guidelines. This involves ensuring consistency and accuracy across large data volumes and flagging annotation issues to maintain high data quality.