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Language Data Annotation Jobs (NOW HIRING)

TEKsystems

Data Annotation Specialist

Seattle, WA · Remote

$20/hr

Additional language skills, which are beneficial for multilingual data annotation projects. Proven track record of handling confidential and sensitive information with integrity. This role is ideal ...

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Language Data Annotation information

What is language data annotation?

Language Data Annotation is the process of labeling or tagging linguistic data such as text, audio, or video with relevant information to make it understandable for machines. This often involves tasks like tagging parts of speech, identifying named entities, or transcribing spoken words. The annotated data is then used to train, validate, and test language models and other AI systems, improving their ability to understand and process human language. Language data annotators play a crucial role in developing technologies like chatbots, voice assistants, and translation services.

What skills and qualifications are needed to thrive as a language data annotator?

To thrive as a Language Data Annotator, you need strong linguistic skills, attention to detail, and familiarity with language structures, often supported by a background in linguistics or a related field. Experience with annotation tools, text labeling platforms, and sometimes scripting languages like Python is typically required. Excellent communication, critical thinking, and the ability to follow complex guidelines help annotators produce high-quality, consistent data. These skills ensure that annotated datasets are accurate and reliable, which is crucial for developing effective natural language processing models.

What challenges do language data annotators face, and how can they be managed?

Language data annotators often encounter challenges such as handling ambiguous text, maintaining consistency across large datasets, and meeting tight deadlines. Ambiguity can arise from slang, idioms, or context-dependent meanings, requiring annotators to use clear guidelines and sometimes consult with team leads or linguists. Consistency is managed through regular calibration meetings and quality checks. Collaborating closely with team members and leveraging annotation tools also helps streamline workflows and uphold high-quality standards.

What is the difference between Language Data Annotation vs Data Labeling Specialist?

AspectLanguage Data AnnotationData Labeling Specialist
Primary FocusAnnotating language data such as text, speech, and transcriptsLabeling various data types including images, videos, and audio
Skills RequiredLanguage proficiency, linguistic knowledge, annotation toolsGeneral labeling tools, data understanding, attention to detail
Work EnvironmentData annotation platforms, remote or office-basedData labeling platforms, remote or office-based
Industry UsageNatural language processing, speech recognition, AI trainingComputer vision, autonomous vehicles, AI datasets

Language Data Annotation specialists focus on preparing language-related data for AI models, emphasizing linguistic accuracy. Data Labeling Specialists work across various data types, including images and videos, to help train machine learning algorithms. While both roles involve data annotation, their specific focus and skill sets differ based on data type and application.

What other helpful pages are available for Language Data Annotation?

Other pages related to Language Data Annotation:

Infographic showing various Language Data Annotation job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 85% Physical, 3% Hybrid, and 12% Remote job distribution.

Data Annotation Engineer

Louisville, KY • On-site

Tanisha Systems, Inc.
1 - 5K employees
Learn more about Tanisha Systems, Inc.external

Other

Posted 12 days ago

Job description

Data Annotation Engineer
Location - Louisville, Kentucky (Day 1 onsite)
Pay Rate: Market- based on experience
We are looking for an Annotation Program Lead to manage our AI Quality Annotation Program. This individual will oversee the operational execution of human review activities that establish the gold-standard datasets used to measure conversational AI performance, safety, and compliance.
The ideal candidate combines strong program management skills with experience in quality assurance, data annotation operations, and stakeholder engagement.
  • Lead the day-to-day operation of the AI annotation program.
  • Manage a team of annotators responsible for reviewing customer conversations and AI interactions.
  • Develop annotation guidelines, procedures, and quality standards.
  • Establish calibration programs and quality assurance processes to ensure consistency across reviewers.
  • Partner with Data Scientists to create and maintain gold-standard datasets.
  • Monitor annotation accuracy, throughput, and quality metrics.
  • Coordinate reporting and deliverables for Legal, Compliance, Responsible AI, and executive stakeholders.
  • Manage annotation workflows and tooling including setting up annotation jobs, running data processing scripts, and testing annotation UIs for quality before job launch.
  • Plan capacity requirements to support seasonal increases in review volume.
  • Support future expansion into multilingual evaluation programs.
  • Identify process improvements that increase efficiency and annotation quality.
  • Bachelor's degree or equivalent experience in Linguistics/Psychology.
  • 5+ years of experience in program management, operations, quality assurance, data labeling, or related fields.
  • Experience leading teams and managing operational workflows.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Strong organizational and stakeholder management skills.
  • Ability to analyze quality metrics and drive continuous improvement initiatives.
  • Excellent written and verbal communication skills.
  • Familiarity with Python/R, SQL and Excel. Should be familiar with running scripts on language data.
  • Experience supporting AI, machine learning, or data annotation programs.
  • Familiarity with Responsible AI, compliance, legal review, or governance processes.
  • Experience developing operational standards and quality frameworks.
  • Experience managing vendor or contractor resources.


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