Job Description
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.