Healthcare Data Labeling Specialist & Promt Engineer

We are looking for a detail-oriented Healthcare Data Labeling Specialist & LLM Prompt Engineer who will work closely with clinical text, support AI development workflows, and help improve the performance of our healthcare-focused large language models.
This role combines prompt engineering, healthcare text interpretation, and light data labeling, giving you hands-on experience across the full lifecycle of building clinical-grade AI systems.

Requirements:

• Excellent English reading and writing skills, with the ability to interpret complex or unstructured text.
• 1–3+ years of experience in prompt engineering, NLP, machine learning, or similar fields.
• Strong attention to detail and ability to follow structured guidelines.
• Interest in healthcare, medical documentation, or clinical workflows.
• Clear and proactive communication with technical and non-technical teams.
• Basic familiarity with spreadsheets or annotation tools.
• Proficiency in Python, including working with LLM APIs and simple evaluation scripts.
• Experience handling unstructured text (clinical or technical).
• Bachelor’s degree in Computer Science, Linguistics, Data Science, Cognitive Science, Engineering, or a related field — or equivalent practical experience.

Will be a plus:

• Experience with healthcare notes, EHR/EMR data, clinical terminology, or medical workflows.
• Familiarity with LLM evaluation methodologies (human evaluation, rubric scoring, automated metrics).
• Knowledge of RAG systems, vector databases, or fine-tuning pipelines.
• Experience with analytics libraries (Pandas, NumPy).
• Exposure to medical scribing, transcription, or clinical documentation review.
• Ability to explain clinical scenarios or terminology to non-clinical teammates.

Responsibilities:

• Develop, test, and refine prompts to improve LLM accuracy and reliability.
• Analyze model outputs and implement iterative improvements.
• Review and annotate healthcare notes and clinical documents to support evaluation and dataset creation.
• Follow and contribute to labeling guidelines, taxonomies, and annotation schemas.
• Communicate ambiguities, clinical terminology questions, or structural issues to technical teams.
• Assist in creating fine-tuning datasets and synthetic data generation workflows.
• Document prompt strategies, evaluation metrics, examples, and best practices.
• Ensure full compliance with privacy, security, and healthcare regulations (e.g., HIPAA).
• Collaborate with product, engineering, clinical, and research teams in a fast-paced environment.