OCR/IDP Data Labelling & Validation Specialist - Contract -

Join ABBYY and be part of a team that celebrates your unique work style. With flexible work options, a supportive team, and rewards that reflect your value, you can focus on what matters most – driving your growth, while fueling ours. Our commitment to respect, transparency, and simplicity means you can trust us to always choose to do the right thing. As a trusted partner for purpose-built AI and intelligent automation, we solve highly complex problems for our enterprise customers and put their information to work to transform the way they do business. Over 10,000 customers trust ABBYY, including many Fortune 500 ones. You will work on further developing a portfolio already containing client names such as DHL, Johnson Johnson, FDA, DMV, PwC, KeyBank, Spotify, and HR BLOCK. Important Note This is a project-based contract role with an initial 6-month duration. While contract extensions may be offered based on performance and business needs, this role does not convert to full-time employment unless explicitly stated . Position Overview We are seeking detail-oriented Data Labeling Validation Specialists to support ABBYY’s OCR and Intelligent Document Processing (IDP) systems. This role combines hands-on document annotation with structured validation of automated labeling outputs. You will play a key role in the human-in-the-loop pipeline , ensuring machine learning models are trained on high-quality, accurate ground truth data. Success in this role requires prior hands-on annotation experience and the ability to evaluate whether automated outputs meet quality expectations, identify error patterns, and provide structured feedback to improve model performance. Key Responsibilities Document Annotation Annotate semi-structured and unstructured documents across diverse formats and domains Perform labeling across key IDP elements, including: Text recognition (including handwriting) Document classification Field extraction (PII, dates, amounts, signatures, etc.) Table detection and structure Label document layout elements such as zones, reading order, and hierarchy Verify OCR output accuracy and correct recognition errors Handle complex or ambiguous document formats beyond automated capabilities Maintain high levels of accuracy and consistency across all annotation tasks Auto-Label Validation Error Analysis Review sampled subsets of auto-labeled outputs and validate against ground truth Identify, categorize, and document errors—including distinguishing: Isolated issues Systematic failure patterns across document types Provide structured, actionable feedback to ML engineering teams Assess confidence scores and flag outputs below quality thresholds Track validation metrics over time and identify quality trends Quality Assurance Feedback Review annotations completed by other team members to ensure consistency Identify and document edge cases (e.g., unusual layouts, ambiguous fields) Participate in calibration sessions to align on annotation standards Provide feedback to improve annotation guidelines and workflows Adhere strictly to data privacy and confidentiality standards Qualifications Education Experience High school diploma or equivalent; Associate’s or Bachelor’s degree preferred 1+ year of hands-on experience in document annotation or data labeling (direct annotation required) Proven ability to maintain high accuracy in repetitive, detail-oriented tasks Experience working with and following annotation guidelines Technical Skills Familiarity with annotation tools and labeling platforms Understanding of document structure and layout types Basic knowledge of data privacy and security practices Reliable computer and high-speed internet connection Strong English reading comprehension and written communication skills Analytical Skills Ability to distinguish between isolated errors and systematic issues Strong pattern recognition across large datasets Critical thinking to e ... (truncated, view full listing at source)