Korean PDF Annotation Specialist

Obsidian; New York (NY); On-site; USD 42,000 - 70,000; Full time

Job summary

Mercor is seeking a detail-oriented Korean language annotator for building structured training data. You will open tasks, verify Korean content, and annotate every meaningful region with precise type and reading order.

You will transcribe faithfully in Hangul, including hanja where present, and document metadata. The role requires native Korean fluency and a meticulous reviewer-velocity process to ensure quality on every page.

Qualifications

• Native Korean speaker with full Hangul command.
• Experience in bilingual transcription, editorial work, or AI training data preferred.
• Character-level accuracy is essential; careful and methodical work.
• Systematic approach to annotate hundreds of pages.
• Review by a second Korean expert is part of the process.

Responsibilities

• Open and verify tasks, ensure pages are Korean, legible, and free of personal details.
• Annotate structure and bound regions with types and reading order.
• Record relationships between regions and figures/tables.
• Transcribe faithfully in Hangul, noting illegible areas.
• Capture page metadata such as language, document type, and dimensions.
• Participate in peer review to ensure quality first-time.

Skills

Korean language

Attention to detail

Editing/review experience

Job description

Fluent Language Skills Required:

Korean. Native fluency in Korean, including full command of Hangul, is required for this position. All annotation and transcription work is performed in Korean.

Why This Role Exists

Document understanding breaks down fastest in the languages that parsing and vision-language models rarely see. This project builds training data for exactly those languages: Korean, alongside Japanese and five Indic scripts. Each task takes a real, publicly available PDF page and produces a complete structural map of that page, paired with a faithful transcription of every text region in the original script.

The dataset deliberately concentrates on the material models handle worst: handwriting, dense multi-column layouts, tables, diagrams, and mixed-script pages. Documents are drawn from newspapers, textbooks, examinations, and everyday formats such as flyers, forms, manuals, menus, brochures, notices and worksheets, so that the corpus reflects the real diversity of Korean documents rather than a narrow band of easily parsed ones.

Delivered work is human-authored throughout. Component identification, component typing, reading order and all transcription are performed by people, not generated by parsing models.

What You'll Do

• Open and check a task: pages are provided, so you do not source documents yourself. We find the PDFs and upload them for you. Before annotating, confirm the page is in Korean, is legible, has real content, and shows no personal details

• Annotate structure: identify and bound every meaningful region of the page - document title, section heading, paragraph, list, table, figure, diagram, caption, formula, question, answer field - and assign each a component type and a reading-order index

• Record relationships: link each region to the figure or table it belongs to through a parent component identifier

• Transcribe faithfully: reproduce all text exactly as it appears in Hangul, including any hanja and handwritten content, flagging any region where the source is not legible

• Capture page metadata: 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, and experienced annotators take on that review

Who You Are

• You are a native Korean speaker with full command of Hangul, including hanja where it appears in older or formal documents

• You have worked in bilingual transcription, translation, editorial work, or AI training data, ideally with reviewer experience

• You are exact: character-level accuracy matters more here than speed, and a single wrong jamo is a defect

• You are systematic: you apply a taxonomy consistently across hundreds of pages rather than improvising per document

• You are comfortable with unfamiliar layouts: multi-column newspapers, exam papers, handwritten forms

Nice-to-Have Specialties

• AI training data: annotation, labeling, grading, or bilingual evaluation for training datasets

• Transcription and localization: MTPE, subtitling, bilingual QA, OCR correction or post-editing

• Document production: typesetting, copy-editing, proofreading, or digitization of Korean-language material

• Script and encoding: Unicode normalization, Korean input methods, Hangul jamo composition, and hanja handling

What Success Looks Like

• Every meaningful region on the page is captured, correctly bounded and correctly typed

• Reading order reflects how the page is actually read, including across columns

• Transcriptions match the source character for character, in Hangul rather than romanization

• Your tasks pass second-expert review the first time

• Unsuitable pages are flagged up front rather than after thirty minutes of work

Why Join Mercor

• Build the training data that makes document AI work in scripts it currently handles badly

• Work from real published Korean documents rather than synthetic or templated pages

• Quality leads on this project: accuracy is the first measure, with handling time tracked alongside it