Korean Transcription Expert
Pubblicato il 13-10-2026 - Mercor in Montà
About the job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey.
Position: PDF Annotation & Transcription Experts – Korean Type: Contract Compensation: $34/hour Location: Remote
Role Responsibilities
- Source documents by finding publicly accessible Korean PDFs with multimodal elements like images, tables, or diagrams. Record the source of each document.
- Annotate structure by identifying and bounding every meaningful region of the page. Assign each a component type and a reading-order index.
- Record relationships by linking each region to its parent component, such as a figure or table.
- Transcribe faithfully by reproducing all text exactly as it appears in Hangul , including any hanja and handwritten content. Flag any illegible regions.
- Capture page metadata, including language, document type, source, page dimensions, and flags for tables, formulas, and handwriting.
- Review a colleague's work.
Every task is reviewed end-to-end by a second Korean expert.
Qualifications Must-Have
- Native Korean speaker with full command of Hangul , including hanja.
- Experience in bilingual transcription, translation, editorial work, or AI training data.
- Exact character-level accuracy.
- Systematic application of taxonomy across pages.
- Comfort with unfamiliar layouts like multi-column newspapers and handwritten forms.
Preferred
- Experience with AI training data : annotation, labeling, grading, or bilingual evaluation.
- Skills in transcription and localization: MTPE , subtitling, bilingual QA, OCR correction.
- Document production experience: typesetting, copy-editing, proofreading, or digitization of Korean-language material.
- Knowledge of script and encoding: Unicode normalization, Korean input methods, Hangul jamo composition, and hanja handling.
Application Process
(Takes 20–30 mins to