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Data Annotation Specialist Jobs (NOW HIRING)

TEKsystems

Data Annotation Specialist

Seattle, WA · Remote

$20/hr

Prior experience in content moderation, data annotation, or policy enforcement roles. Technical Skills Proficiency with annotation tools and platforms. Ability to review multimedia content (text ...

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Data Annotation Specialist information

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$28K

$72.9K

$88K

How much do data annotation specialist jobs pay per year?

As of Jul 22, 2026, the average yearly pay for data annotation specialist in the United States is $72,947.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,000.00 and $87,000.00 per year, depending on experience, location, and employer.

What is the salary of data annotation specialist?

The average salary of a data annotation specialist typically ranges from $30,000 to $60,000 per year, depending on experience, location, and the complexity of annotation tasks. Entry-level roles may start lower, while experienced specialists working with advanced tools or in high-demand industries can earn higher wages.

What does a data annotation specialist do?

A data annotation specialist labels and tags data such as images, text, or videos to help train machine learning models. They use tools and follow guidelines to ensure data is accurately annotated, which is essential for developing AI applications. Attention to detail and understanding of data types are important in this role.

What are Data Annotation Specialists?

Data Annotation Specialists are professionals who label and categorize data—such as images, text, audio, or video—to make it usable for machine learning models and artificial intelligence systems. Their work ensures that algorithms can accurately interpret data by providing clear examples of what different data points represent. Tasks may include drawing bounding boxes on images, transcribing audio, or tagging keywords in text. This role is crucial for improving the accuracy and reliability of AI applications across various industries.

What are some common challenges a Data Annotation Specialist faces, and how can they be addressed?

Data Annotation Specialists often encounter challenges such as maintaining high accuracy while labeling large volumes of data, managing repetitive tasks, and understanding complex annotation guidelines. To overcome these, it's important to stay detail-oriented, take regular breaks to avoid fatigue, and seek clarification on ambiguous instructions. Collaborating with team members and participating in quality review sessions can also help ensure consistency and improve annotation quality over time.

What is the difference between Data Annotation Specialist vs Data Labeler?

AspectData Annotation SpecialistData Labeler
CredentialsHigh school diploma or equivalent; some roles may prefer certifications in data management or annotation toolsTypically high school diploma or equivalent; minimal formal requirements
Work EnvironmentOffice or remote; often involves using specialized annotation softwarePrimarily remote or in-house; focuses on labeling data within specific datasets
Industry UsageUsed across AI, machine learning, and data science projectsPrimarily in AI and machine learning industries for training data
Job FocusInvolves detailed annotation, quality control, and understanding project guidelinesFocuses on labeling data accurately according to instructions

While both roles involve working with data to train AI models, Data Annotation Specialists typically handle more complex annotation tasks and quality assurance, whereas Data Labelers focus on straightforward labeling tasks. The Specialist role often requires a deeper understanding of project guidelines and may involve using advanced annotation tools.

How hard is it to get hired by data annotation?

Getting hired as a data annotation specialist generally requires basic computer skills, attention to detail, and familiarity with annotation tools. Many positions are entry-level and may not require prior experience, but strong accuracy and the ability to follow guidelines are important for success.

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist, and why are they important?

To thrive as a Data Annotation Specialist, you need a keen attention to detail, strong analytical abilities, and basic knowledge of data labeling concepts, often supported by a high school diploma or relevant coursework. Familiarity with data annotation tools (such as Labelbox or Supervisely), basic computer skills, and sometimes an understanding of programming languages like Python are valuable. Excellent communication, time management, and the ability to follow guidelines precisely help you stand out in this position. These skills ensure accurate and consistent data labeling, which is essential for training reliable machine learning models.

Does data annotation really pay?

Data annotation specialists can earn varying wages depending on experience, project complexity, and platform. Entry-level roles may pay hourly or per task rates, often ranging from a few dollars to over twenty dollars per hour, with more experienced annotators earning higher rates. Payment is typically processed through online platforms or companies that hire remote workers for annotation tasks.
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Data Annotation Specialist jobs near you
Infographic showing various Data Annotation Specialist job openings in the United States as of July 2026, with employment types broken down into 3% Locum Tenens, 23% Full Time, 28% Part Time, 1% Contract, 44% Nights, and 1% Summer. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution, with an average salary of $72,947 per year, or $35.1 per hour.

Data Annotation Specialist

MphasiS Corporation USA

Palo Alto, CA • On-site

Other

Posted 6 days ago

Job description

Job Summary

Review user queries and assign the correct label based on provided guidelines. Ensure annotations are accurate, consistent, and meet quality standards.

Key Responsibilities

  • Read and review user queries.
  • Assign the correct annotation label.
  • Verify and correct existing labels when needed.
  • Follow annotation guidelines and quality rules.
  • Flag or comment on unclear or complex cases.
  • Maintain accuracy and consistency in all annotations.

Required Skills

  • Good English reading and comprehension.
  • Strong attention to detail.
  • Basic analytical and decision-making skills.
  • Ability to follow written guidelines.
  • Basic computer knowledge.
  • Familiarity with Microsoft Excel or spreadsheets.

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