Healthcare Data Labeling Specialist (Junior)

We are seeking a detail-oriented Healthcare Data Labeling Specialist to support our AI and prompt engineering teams by creating accurate ground-truth labels for healthcare notes and clinical text. This role plays a critical part in improving clinical-grade AI models by ensuring the highest quality annotations, clarifications, and data interpretation.

     
     We offer:

• Flexible working hours;
• Paid vacation and sick days;
• Health insurance;
• Professional growth;
• Internal English classes and compensation for educational courses;
• Professional accountant and lawyer;
• Friendly atmosphere.
  
  Requirements:
• Exceptional English reading and writing skills, with the ability to interpret nuanced and sometimes unstructured clinical language.
• Strong attention to detail and ability to follow structured labeling guidelines.
• Interest in healthcare, medical documentation, or clinical workflows.
• Ability to communicate clearly with technical teams and ask clarifying questions when needed.
• Comfortable working with repetitive or detail-oriented tasks while maintaining accuracy.

• Basic familiarity with spreadsheets or labeling tools.

  
  Will be a plus: 

• Experience in medical scribing, medical transcription, clinical documentation review, or healthcare support roles.
• Coursework or background in a related field (e.g., biology, public health, pre-med, nursing, psychology).
• Familiarity with EMRs/EHRs or common clinical terminology.
• Experience with data labeling tools or annotation platforms.

• Ability to understand and explain clinical scenarios or terminology to non-clinical team members.

  
  Responsibilities:

• Review, interpret, and annotate healthcare notes, clinical documents, and unstructured medical text with precise ground-truth labels.
• Follow detailed labeling guidelines and contribute to improving them as data complexity evolves.
• Collaborate with prompt engineers to clarify clinical scenarios, document structure, terminology, and edge cases.
• Provide written explanations or examples that help engineers refine LLM prompts and evaluation workflows.
• Perform quality checks on labeled data to ensure consistency and accuracy.
• Maintain strict adherence to data privacy and PHI-handling standards (e.g., HIPAA).
• Communicate ambiguities, inconsistencies, or missing information in data to engineering teams.
• Assist in building reference sets, taxonomies, and annotation schemas for clinical NLP tasks.