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Data Annotation For Ai Jobs (NOW HIRING)

NEURA Robotics

Your mission & challenges As an AI Data Annotation Specialist, you will operate at the intersection ... Your primary responsibility is to design and maintain scalable workflows for automated data ...

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Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

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Infographic showing various Data Annotation For Ai job openings in the United States as of September 2026, with employment types broken down into 70% Full Time, 15% Part Time, and 15% Contract. Highlights an 60% In-person, 10% Hybrid, and 30% 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 14 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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