Clinical Data Annotation Specialist (Remote, Project-Based)
Perle
2 horas atrás
•Nenhuma candidatura
Sobre
- The Opportunity
- We’re seeking Physicians, Dentists, and Pharmacists to join us as Clinical Data Annotation Specialists on a remote, project-based basis. In this role, your clinical expertise will directly influence the development of AI models used across large-scale healthcare partnerships.
- If you're excited about applying your medical background in the world of cutting-edge health technology, this opportunity lets you make a meaningful impact from anywhere in the world—while opening the door to long-term career growth within clinical AI.
- What you'll do
- Annotate and label clinical data with accuracy, including symptoms, diagnoses, medications, patient histories, and clinical notes.
- Identify early disease indicators, capturing nuanced patterns that help AI systems understand health risks earlier.
- Validate data quality and ensure consistency across annotations.
- Work with Perle’s AI and product teams to refine annotation guidelines and create clinically meaningful labeling frameworks.
- Offer clinical interpretation for complex or ambiguous cases.
- Document your rationale clearly and consistently to support model reliability.
- Qualifications
A clinical degree in one of the following areas
- Medical (e.g., MBBS/MD)
- Dental (e.g., BDS/DDS/DMD)
- Pharmaceutical (e.g., BPharm/PharmD)
- Solid understanding of common clinical terms, diagnostics, and healthcare workflows.
- Ability to interpret real medical records with strong attention to detail.
- Comfort identifying early-stage disease patterns or subtle clinical risk factors.
- Strong written communication and dependable clinical reasoning.
- Ability to work independently and manage project-based tasks.
- Openness to feedback and evolving guidelines.
- Bonus points for: Experience in medical documentation, clinical research, healthcare analytics, or medical coding; Exposure to AI/ML projects or data annotation tools; Familiarity with EHR systems and structured/unstructured medical data.





