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Data Labeling Jobs (NOW HIRING)

Position Summary The Data Labeling Specialist is responsible for carefully reviewing healthcare-related conversations to determine whether a safety event has occurred. This role is highly ...

๐Ÿš€ Data Labeling Specialist - AI & Robotics ๐Ÿ’ก No prior experience required -- All training will be provided Join our mission to build the world's first general-purpose humanoid robot. As a Data ...

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Data Labeling information

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How much do data labeling jobs pay per hour?

As of Jun 1, 2026, the average hourly pay for data labeling in the United States is $24.51, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $28.12 per hour, depending on experience, location, and employer.

What is a Data Labeling job?

A Data Labeling job involves annotating or tagging data, such as images, text, audio, or videos, to help train machine learning models. Labelers follow specific guidelines to classify data accurately so that AI systems can learn patterns and make predictions. This role is essential in fields like computer vision, natural language processing, and speech recognition. Strong attention to detail and consistency are crucial for ensuring high-quality training datasets.

What are the key skills and qualifications needed to thrive in the Data Labeling position, and why are they important?

To thrive in Data Labeling, you need meticulous attention to detail, strong analytical abilities, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with data annotation tools, image or text editing software, and experience with platforms like Labelbox or Amazon SageMaker Ground Truth are commonly advantageous. Exceptional concentration, patience, and the ability to follow precise instructions are valuable soft skills in this position. These skills and qualities are essential for ensuring the accuracy and consistency of labeled datasets, which are critical for training reliable AI and machine learning models.

What are the typical day-to-day responsibilities of a Data Labeling professional?

A Data Labeling professional is primarily responsible for reviewing and accurately tagging images, text, audio, or video data according to specified guidelines. Daily tasks often include managing large datasets, using annotation software to classify data, and verifying the quality and accuracy of the labels. Collaboration with data scientists, project managers, and other annotators is common, especially when clarifying labeling guidelines or resolving ambiguities. Attention to detail is crucial, as high-quality labeled data directly impacts the effectiveness of machine learning models and AI applications. Most positions are structured in team environments, where productivity and communication skills help ensure project deadlines are met.
What cities are hiring for Data Labeling jobs? Cities with the most Data Labeling job openings:
What are the most commonly searched types of Data Labeling jobs? The most popular types of Data Labeling jobs are:
What states have the most Data Labeling jobs? States with the most job openings for Data Labeling jobs include:
Infographic showing various Data Labeling job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, and 4% Contract. Highlights an 25% Physical, and 75% Remote job distribution, with an average salary of $50,981 per year, or $24.5 per hour.

Data Labeling Specialist

Authenticx

Indianapolis, IN โ€ข Remote

Other

Posted 11 days ago


Job description

Position Summary

The Data Labeling Specialist is responsible for carefully reviewing healthcare-related conversations to determine whether a safety event has occurred. This role is highly transactional and relies on consistently applying a well-defined rubric to ensure accurate and objective identification of safety-related concerns. Your work is critical in supporting healthcare clients in maintaining compliance and improving outcomes. This role requires high accuracy and attention to detail while labeling high volumes of patient conversations.

Key Responsibilities

  • Conversation Review: Evaluate a high volume of healthcare-related conversations, using established rubrics to determine the presence or absence of safety events.
  • Rubric Adherence: Apply clearly defined labeling criteria with consistency and discipline to ensure reliability in safety event detection.
  • Quality and Accuracy: Deliver precise and objective reviews that align with expectations for labeling accuracy, supporting broader data integrity.
  • Team Collaboration: Participate in calibration efforts with teammates to promote labeling alignment across the team.
  • Feedback Adoption: Incorporate feedback from audits and performance checks to continually refine review accuracy and consistency.

Success Criteria:

  • Consistently apply rubric criteria to produce high-quality, objective safety labels validated through regular audits.
  • Achieve strong alignment with team standards and calibration practices.
  • Meet or exceed performance targets for daily and weekly review volumes while maintaining quality benchmarks.
  • Demonstrate reliability and consistency in handling high-volume, repetitive work while maintaining accuracy.

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Key Skills and Abilities:

  • Objectivity: Strong ability to apply standards without bias or interpretation, even under repetitive conditions.
  • Attention to Detail: Exceptional focus on minute details to ensure safety flags are accurately identified.
  • Rubric-Driven Thinking: Comfort and discipline in working within a structured rubric-based decision framework.
  • Repetition Tolerance: High tolerance for performing repetitive tasks at scale while maintaining focus and precision.
  • Critical Thinking: Ability to assess edge cases within rubric guidelines to make consistent, sound judgments.
  • Quality Mindset: Motivated by accuracy and the importance of contributing to patient safety through diligence and care.
  • Written Communication: Capable of documenting decisions clearly and concisely when necessary.

Qualifications

  • 1-3 years of experience in customer support, health care, compliance, quality assurance or similar fields.
  • Strong analytical and critical thinking skills.
  • Ability to perform repetitive tasks with high attention to detail.
  • Experience in data labeling, transcription review, AI development or working with AI data is a plus.
  • Experience with pharmacovigilance is especially welcome.

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Work Environment

The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.

  • This is a remote / virtual position

Physical Demands

The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee:

  • Is regularly required to sit and use hands to type and operate a computer and phone
  • Is frequently required to talk and hear
  • Is occasionally required to stand and walk
  • Must occasionally lift and/or move up to 25 pounds
  • Is occasionally required to reach with hands and arms, stoop, kneel, or crouch

Other

  • Time Management: Ability to manage time efficiently, balancing multiple projects and meeting tight deadlines.
  • Team Collaboration: Experience working cross-functionally with other teams (e.g., AI, data science) to achieve shared goals.
  • Problem-Solving: Strong problem-solving skills to identify inconsistencies or issues in labeling and find effective solutions.
  • Adaptability: Ability to adapt to changing priorities and project requirements.
  • Written Communication: Strong written communication skills to clearly document rubrics and provide detailed feedback during audits.
  • Technical Aptitude: Familiarity with data labeling software, spreadsheets, or other data management tools.