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$34K

$82.6K

$136K

How much do volunteer data labeling analyst jobs pay per year?

As of Jul 15, 2026, the average yearly pay for volunteer data labeling analyst in the United States is $82,640.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,500.00 and $97,000.00 per year, depending on experience, location, and employer.

What does a Volunteer Data Labeling Analyst do?

A Volunteer Data Labeling Analyst assists organizations by reviewing, categorizing, and tagging data—such as images, text, or audio—to help train machine learning models. Their work ensures that the data used for artificial intelligence projects is accurate and well-organized. Volunteers in this role may annotate various types of data, correct errors, and maintain quality standards according to project guidelines. This position is often remote and can be a great way to gain experience in the data science field while supporting important research and development.

What is the difference between Volunteer Data Labeling Analyst vs Data Annotator?

AspectVolunteer Data Labeling AnalystData Annotator
CredentialsBasic computer skills, sometimes training providedBasic computer skills, often on-the-job training
Work EnvironmentRemote or flexible, volunteer-basedRemote or on-site, paid position
Industry UsageUsed in AI/ML projects, volunteer platformsUsed in AI/ML, tech companies, research

The Volunteer Data Labeling Analyst and Data Annotator roles both involve labeling data for machine learning models. The main difference lies in compensation and commitment: Volunteer Data Labeling Analysts typically work on a volunteer basis, often with flexible hours, while Data Annotators are paid employees or contractors. Both roles require similar skills and are used across AI and tech industries, but the volunteer role emphasizes community contribution without monetary compensation.

What are the typical collaboration opportunities for a Volunteer Data Labeling Analyst within a project team?

As a Volunteer Data Labeling Analyst, you’ll often work closely with data scientists, machine learning engineers, and project managers to ensure that raw data is accurately categorized for model training. Collaboration typically involves regular check-ins to align on labeling guidelines, resolve ambiguities, and provide feedback on data quality. You may also participate in team meetings to discuss project progress and address any challenges encountered in the labeling process. This collaborative environment not only supports your learning but also helps the team build more robust AI models.

What are the key skills and qualifications needed to thrive as a Volunteer Data Labeling Analyst, and why are they important?

To thrive as a Volunteer Data Labeling Analyst, you need attention to detail, basic data handling skills, and familiarity with data annotation concepts, often supported by a high school diploma or equivalent. Experience with data labeling platforms, spreadsheets, and annotation tools like Labelbox or CVAT is typically required. Strong communication, reliability, and the ability to follow instructions help someone excel in collaborative and quality-focused environments. These skills ensure accurate and consistent data labeling, which is critical for developing robust machine learning models.
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Volunteer Data Labeling Analyst jobs near you
Czech Data Labeling Analyst(Speech & Voice )

Czech Data Labeling Analyst(Speech & Voice )

Welocalize

Washington, DC

$26 - $28/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 6 days ago

Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

361st of 451 rated business services

Job description

Overview

Welo Data is looking for detail-oriented and reliable individuals to join our team as Data Labeling Analysts, supporting speech and voice AI systems.

This is a high-impact production role focused on building the datasets that power real-world AI systems. You’ll be working with audio, speech, and language data — helping ensure models are trained on accurate, well-structured, and representative inputs.

While this role is more execution-focused than evaluation-heavy roles, it still requires strong judgment, attention to detail, and consistency. The work sits at the intersection of language, data, and AI systems — where precision and discipline matter at scale.

We’re looking for people who are dependable, focused, and take pride in producing high-quality work, even across repetitive workflows.

Project Details
  • Job Title: Data Labeling Analyst
  • Hiring in: Onsite (Bay Area, Seattle, NYC, or client-dependent)
  • Hours: Full-time, 40 hours per week
  • Employment Type: W2 Full-Time Employee
  • Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
  • Pay Rate: $26 - $28/hour

Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: New York City, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, or Burlingame. Please only apply if you meet this location requirement.

What You'll Do
  • Execute high-volume data labeling and annotation tasks across speech and voice datasets
  • Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale
  • Work with audio and language data, including transcription, categorization, and tagging
  • Maintain strong throughput while meeting quality expectations
  • Escalate unclear or ambiguous cases appropriately
  • Adapt to evolving guidelines and workflows as systems and requirements change
  • Support baseline data production needs for AI training pipelines
  • Contribute to team calibrations and quality alignment sessions
What We're Looking For
  • Native-level fluency in Croatian
  • Strong written communication skills and language fundamentals
  • 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field.
  • Ability to follow detailed instructions and apply guidelines consistently
  • High attention to detail and ability to maintain accuracy in repetitive tasks
  • Comfort working in structured, process-driven environments
  • Ability to manage time effectively and maintain steady output
  • Willingness to ask questions and escalate when needed
  • Basic familiarity with AI, speech technology, or language data is a plus
Benefits
  • Paid Vacation: 6 days
  • Paid Company Holidays: 2 days (Memorial Day and Labor Day)
  • Paid Sick Leave: accrued per applicable state law and company policy
  • Medical, Dental, and Vision Insurance (eligibility applies)
  • Health Savings Account (HSA)
  • 401(k) Retirement Plan
  • Employee Assistance Program
  • Additional voluntary benefits (life, accident, critical illness, etc.)

Onsite Perks (where applicable):
Free breakfast, lunch, and dinner
Stocked micro-kitchens with snacks and beverages
Commuter benefits, including shuttles and bike-to-work options
Unique campus features depending on location

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