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Data Labeler Remote Jobs (NOW HIRING)

TSMG

AI/ML Data Contributor

Los Angeles, CA · Remote

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

Recruitment Room

$20/hr

Remote Compensation: $20 per hour Openings: 600 Role Overview We are seeking detail-oriented Data ... Your work will involve labeling, categorizing, and annotating diverse datasets to ensure accuracy ...

ICU Medical

Support, Labeling (remote)

Create and maintain label attribute data * Work on special assignments as they arise Knowledge ... This role is based remotely; the incumbent may be remote in any state other than Colorado;

Jobsultant Solutions

Remote Data Analyst

Denver, CO · Remote

Work Environment: * 100% remote - must work in CST * 8am-5pm (9 hour day with one hour lunch break ... Own Brand (private label) reporting, recapping, tracking, and analysis, including ad-hoc analysis ...

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How much do data labeler remote jobs pay per hour?

As of Jul 15, 2026, the average hourly pay for data labeler remote in the United States is $38.68, according to ZipRecruiter salary data. Most workers in this role earn between $33.89 and $43.75 per hour, depending on experience, location, and employer.

Is data labelling a good career?

Data labeling is an entry-level role that involves annotating data for machine learning models, often requiring attention to detail and basic technical skills. It can provide a stepping stone into the tech industry, but it typically offers limited advancement opportunities and lower pay compared to more specialized roles. Many professionals use it as initial experience before moving into data science or related fields.

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

To thrive as a Data Labeler Remote, you need strong attention to detail, basic data analysis skills, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Proficiency with labeling platforms, annotation tools, and sometimes knowledge of spreadsheet software are typically required. Reliability, time management, and effective communication are crucial soft skills for maintaining accuracy and meeting project deadlines in a remote setting. These skills ensure high-quality, consistent labeled data, which is essential for training reliable machine learning models.

How can I make 2000 a week working from home?

A remote data labeler can potentially earn around $2000 per week by working full-time hours, often 40 hours or more, and gaining experience or specializing in high-demand data annotation tasks. Increasing earnings may involve working for multiple clients, improving skills with annotation tools, or taking on higher-paying projects, but consistent high weekly income depends on workload, rates, and efficiency.

What are some common challenges faced by remote data labelers and how can they be managed?

Remote data labelers often encounter challenges such as maintaining focus during repetitive tasks, ensuring consistent annotation quality, and communicating effectively with distributed teams. To manage these, it's helpful to establish a structured work routine, take regular breaks to prevent fatigue, and use annotation guidelines provided by employers. Leveraging collaboration tools for feedback and clarification also helps maintain high-quality output and fosters a sense of connection with team members.

What does a remote data labeler do?

A remote data labeler is responsible for annotating or tagging data—such as images, videos, audio, or text—from a remote location, typically working from home. Their work helps train machine learning models by providing accurate, labeled datasets that algorithms use to learn and make predictions. Data labelers follow specific guidelines to ensure consistency and accuracy, and may use specialized software tools to complete their tasks. This role is essential in industries like artificial intelligence, self-driving cars, and natural language processing. Remote data labelers often work as freelancers or as part of distributed teams for tech companies.

What is the difference between Data Labeler Remote vs Data Annotator Remote?

AspectData Labeler RemoteData Annotator Remote
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote, flexible hours
Industry UsageCommon in AI/ML data preparationCommon in AI/ML data preparation
Job FocusLabeling data points for machine learningAnnotating data for training AI models

Both Data Labeler Remote and Data Annotator Remote roles involve preparing data for AI and machine learning projects. While the terms are often used interchangeably, Data Labeler Remote typically emphasizes labeling data points, whereas Data Annotator Remote may include more detailed annotation tasks. Both roles require similar skills and are performed remotely, making them accessible for individuals seeking flexible data-related jobs.

How much are data labelers paid?

Data labelers working remotely typically earn between $10 and $20 per hour, depending on experience, complexity of tasks, and the company. Some positions may offer project-based pay or bonuses for accuracy and efficiency.

Is data labeling work from home?

Data labelers often work remotely, as the job typically involves reviewing and annotating data using a computer and internet connection. Many companies offer remote data labeling positions with flexible schedules, requiring basic computer skills and attention to detail.
More about Data Labeler Remote jobs
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Data Labeler Remote jobs near you
Infographic showing various Data Labeler Remote job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $80,460 per year, or $38.7 per hour.

AI/ML Data Contributor

TSMG

Los Angeles, CA • Remote

Full-time

Re-posted 19 days ago

Job description

Project Overview
We are currently hiring AI/ML Data Contributors to support a range of active and upcoming projects across the United States. In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing.

Projects may vary in scope and format, offering both remote and in-person opportunities (such as device or VR testing). This is a flexible, task-based role with the opportunity to participate in multiple projects over time.

Responsibilities
  • Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation
  • Participate in remote assignments or attend on-site sessions when required
  • Follow project guidelines and ensure high-quality task completion
  • Provide feedback and input during testing activities
  • Complete tasks within given timelines
Requirements
  • Must be based in the United States
  • Strong attention to detail and ability to follow instructions
  • Basic computer skills and familiarity with digital tools
  • Reliable internet connection and access to a computer or smartphone
  • Availability to participate in task-based work (schedule may vary)
Nice to Have
  • Previous experience in data annotation, QA, or testing
  • Interest in AI, machine learning, or emerging technologies
What We Offer
  • Paid, flexible task-based work
  • Opportunity to work on innovative AI/ML projects
  • Exposure to cutting-edge technologies (including device and VR testing)
  • Potential for ongoing project participation

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.