Medical Doctor with SNOMED Coding Experience (UK)
About this role
Employer-provided description, formatted for easier reading.
We are seeking qualified medical doctors to annotate and code clinical documents with SNOMED CT, with a focus on accuracy and consistency against detailed coding guidelines.
You will work asynchronously on a dedicated annotation platform, coding de-identified clinical notes and documentation including discharge summaries, lab results and progress notes against SNOMED CT concepts to support the development of clinical AI platforms.
This is a remote, flexible and short-term project.
Key Responsibilities
- Structured Annotation: Identify clinically relevant concepts across discharge summaries, lab results and prognosis notes, and map them to the correct SNOMED CT codes on a dedicated platform.
- Guideline Application: Work to a coding guideline covering hierarchy scope, negation, medication scope, section-level inclusions and compound coding, applied consistently across a large volume of documents.
- Edge Case Handling: Flag ambiguous or contested cases with a short rationale so guidance can be refined.
- Quality Review: Participate in adjudication rounds to resolve disagreements and reach a consensus code set.
Qualifications
License: GMC Registered Doctor based in the UK
Experience: Minimum 1–2 years of clinical practice, with SNOMED CT coding or clinical terminology experience.
Knowledge
Strong grounding in clinical documentation, terminology and coding conventions.
Attention to detail
Proven accuracy and consistency in coding or clinical review work.
Technical proficiency
Comfortable using structured annotation tools and digital platforms.
Language
Native or near-native written English.
Legal Status
You will have the right to work in your country of residence.
You will work as an independent contractor.
Why Join Us?
- Flexible Work Arrangements: Part-time, remote and fully asynchronous.
- Competitive Compensation: Hourly rate, paid weekly
- Professional Development: Hands-on experience in clinical AI development and terminology work, shaping how AI systems interpret clinical records and assign medical codes.