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Data Annotator job description
A Data Annotator is a professional responsible for labeling and categorizing raw data to create high-quality training datasets for machine learning models. This role is essential for developing accurate AI systems by providing the structured data foundation that enables algorithms to learn patterns and make intelligent decisions.
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What is a Data Annotator?
A Data Annotator is a specialized professional who prepares raw data for use in artificial intelligence and machine learning systems. They work with various types of data including text, images, audio, and video, applying precise labels and annotations that help algorithms understand and interpret information patterns. This role serves as the critical human-in-the-loop component that bridges raw data and functional AI models, ensuring that machine learning systems receive properly categorized and contextualized training data. Data Annotators must possess strong attention to detail, domain-specific knowledge depending on the data type, and understanding of annotation guidelines to maintain consistency and accuracy across datasets.
What does a Data Annotator do?
Data Annotators perform meticulous labeling tasks including identifying and tagging objects in images, categorizing text sentiments, transcribing audio content, and marking key data points in various formats. They follow specific annotation guidelines to ensure consistency, verify data quality through review processes, and collaborate with data scientists to refine labeling criteria. Additionally, they maintain detailed documentation of their annotation methodologies, identify and report data quality issues, and continuously update their approach based on feedback from machine learning model performance. Their work directly impacts the accuracy and effectiveness of AI systems across industries such as autonomous vehicles, healthcare diagnostics, and natural language processing applications.
Job Overview
Data Annotator responsible for labeling, categorizing, and annotating various types of data (text, images, video, audio) to train and improve machine learning models. This role requires exceptional attention to detail and consistency in applying annotation guidelines across diverse datasets for AI/ML applications in the US market.
Data Annotator responsibilities include:
1. Accurately label and annotate data according to specific project guidelines and requirements
2. Perform quality checks on annotated data to ensure accuracy and consistency
3. Identify and flag edge cases, outliers, or problematic data samples
4. Maintain detailed documentation of annotation processes and decisions
5. Collaborate with data scientists and ML engineers to refine annotation guidelines
6. Meet daily annotation quotas while maintaining high quality standards
7. Handle sensitive data in compliance with US data privacy regulations (CCPA, etc.)
8. Participate in regular calibration sessions to ensure annotation consistency across team
1. High school diploma or equivalent with strong attention to detail
2. Basic computer proficiency and ability to learn specialized annotation tools
3. Excellent reading comprehension and ability to follow complex instructions
4. Minimum typing speed of 40 WPM with high accuracy
5. Ability to maintain focus on repetitive tasks for extended periods
6. Basic understanding of data classification and categorization principles
7. Legal authorization to work in the United States
Preferred Qualifications
1. Associate's or Bachelor's degree in any field
2. 1+ years of experience in data entry, quality control, or annotation work
3. Familiarity with common annotation platforms (Labelbox, Scale AI, Amazon SageMaker)
4. Basic understanding of machine learning concepts and data requirements
5. Experience working with diverse data types (text, images, audio, video)
6. Previous remote work experience with strong self-discipline
7. Multilingual capabilities for projects requiring non-English data annotation
Bonus Skills
1. Certification in data annotation or quality assurance
2. Programming knowledge (Python, SQL) for automated quality checks
3. Domain expertise in specific industries (healthcare, automotive, finance)
4. Experience with advanced annotation techniques (semantic segmentation, bounding boxes)
5. Understanding of US data privacy laws and compliance requirements
6. Quality assurance or audit experience in data-intensive environments
7. Previous work on large-scale annotation projects for major US tech companies
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