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 H&R 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 evaluate ambiguous cases and escalate appropriately

• High attention to detail when reviewing auto-generated outputs

Preferred

• 1–2 years of experience in OCR, IDP, or document labeling workflows

• Experience with auto-labeling systems or AI-assisted annotation tools

• Background reviewing or auditing machine-generated outputs

• Familiarity with inter-annotator agreement and data quality metrics

• Domain expertise in document-heavy industries (e.g., finance, legal, healthcare)

• Proficiency in languages beyond English

• Experience with spreadsheets, data tracking, or reporting tools

Compensation & Benefits

• Competitive hourly rate (based on location and experience)

• Flexible schedule within project deadlines

• Remote work environment

What You’ll Gain

• Hands-on experience with real-world AI/ML data pipelines

• Direct collaboration with machine learning engineers

• Exposure to auto-labeling systems and document AI technologies

• Development of skills in data quality, validation, and error analysis

• Experience valuable for future roles in ML data operations, QA, or annotation engineering

Training & Support

• Structured onboarding (1–2 weeks) covering tools, workflows, and guidelines

• Ongoing support from project managers and technical teams

• Access to detailed documentation and best practices

• Regular performance feedback with metrics and improvement insights

Project Details

• Duration: 6-month contract (renewal based on performance and project needs)

• Workload: Typically 20–40 hours per week depending on project phase

• Team Structure: Distributed team with established communication channels

• Performance Metrics:

• Annotation accuracy

• Validation throughput

• Quality of error documentation

• Adherence to guidelines

Application Requirements

Please submit:

• Resume highlighting relevant annotation, data labeling, or QA experience

• Cover letter describing your approach to identifying errors in automated outputs

• Work samples (if available) demonstrating document labeling or review accuracy

Join ABBYY, and you will:

Love how you work

• We provide remote and hybrid working options to fit all lifestyles.
• We use flexible hours across most of our teams to allow you to find your own definition of balance.
• Encouraging a culture of giving, we provide two paid volunteering days off every year so you can take time to contribute to the causes you care about.
• To ensure your family is cared for, we offer paid parental leave in all our locations.

Love whom you work with

• We are a global team of 600+ colleagues, spread across 15 countries on four continents.
• With colleagues representing 30+ nationalities, our workforce reflects the world.
• Innovation and excellence run through our veins. Our teams gather the expertise which has garnered ABBYY more than 140 technology patents.
• We are guided by the values of respect, transparency, and simplicity.
• "Team Environment" is in the top three highest-scoring drivers of engagement across all of our departments.

Love what you work on

• We are a company with more than 35 years of experience in the technology market;
• Over 10,000 customers trust ABBYY, including many Fortune 500 ones, with names such as DHL, Johnson & Johnson, FDA, DMV, PwC, KeyBank, Spotify, and H&R BLOCK;
• We have modernized the capture market by creating the first low-code/no-code IDP platform.
• Our Machine Learning, Natural Language Processing, Computer Vision Technologies, and a marketplace built with AI, can transform any document in any process;
• Top Analyst firms recognize ABBYY's market leadership, including Gartner, Everest PEAK Matrix ® Assessment, ISG Intelligent Automation Lens, and NelsonHall, amongst others.

ABBYY is an Equal Employment Opportunity employer that values the strength that diversity brings to the workplace. To learn more about our commitment to Diversity and Inclusion, check out the careers section on our website.