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Data Annotation For Ai Jobs in Georgia (NOW HIRING)

Smarsh

Lead Data Scientist

Atlanta, GA

... looking for optimization and improvement opportunities * Data annotation and quality review ... We use the latest in AI/ML technology to help our customers break new ground at scale. We are a ...

Smarsh

Lead Data Scientist

Atlanta, GA · On-site

... looking for optimization and improvement opportunities * Data annotation and quality review ... We use the latest in AI/ML technology to help our customers break new ground at scale. We are a ...

Deloitte

Agentic AI, AI & Data Science Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Recruiting for this role ends on 7/31/2026. Work you'll do As an AI and Data Science Engineer III on the AI & Data team, you will be responsible for driving technology-focused client delivery across ...

Deloitte

Agentic AI, AI & Data Science Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Recruiting for this role ends on 7/31/2026. Work you'll do As an AI and Data Science Engineer III on the AI & Data team, you will be responsible for driving technology-focused client delivery across ...

Honeywell

Sr Data Engineer

Atlanta, GA

$110K - $132K/yr

Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows * Develop and optimize RAG (Retrieval Augmented ...

Honeywell

Sr Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows * Develop and optimize RAG (Retrieval Augmented ...

Honeywell

Sr Data Engineer

Atlanta, GA · On-site

$110K - $132K/yr

Build and maintain data pipelines for AI product lifecycle, including training data preparation, feature engineering, and inference data flows * Develop and optimize RAG (Retrieval Augmented ...

Honeywell

Lead Data Engineer

Atlanta, GA · On-site

Define technical direction for AI-ready data products including vector stores, embedding pipelines, and RAG-ready structured/unstructured data. * Adopt emerging LLM orchestration frameworks ...

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Data Annotation For Ai information

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.

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 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 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.
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Data Annotation For Ai jobs near you
Infographic showing various Data Annotation For Ai job openings in Georgia as of July 2026, with employment types broken down into 74% Full Time, 22% Part Time, 1% Temporary, and 3% Contract. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution.

English (US) Audio QA Annotation Specialist

MatchaTalent

Atlanta, GA • On-site, Remote

Full-time

Posted 17 days ago

Job description

This role requires the candidate to work remotely from the United States.


Client Overview

Our client is a global artificial intelligence technology company specializing in the development of advanced large language models (LLMs), speech recognition technologies, multilingual AI systems, and data annotation solutions. The organization collaborates with leading AI research laboratories and enterprise technology companies worldwide to accelerate the development of next-generation artificial intelligence through high-quality human-generated data.

Supporting a diverse portfolio of multilingual AI initiatives, the company works with language specialists, voice professionals, and annotation experts across the globe to improve the accuracy, contextual understanding, and performance of cutting-edge AI systems used in speech processing, conversational AI, and natural language understanding.


Job Role

The English (US) Audio QA Annotation Specialist is responsible for reviewing, evaluating, and validating English (US) audio recordings to ensure they meet the highest quality standards required for AI speech recognition and audio annotation projects.

Working as part of a multilingual quality assurance team, this role focuses on assessing recording accuracy, pronunciation, fluency, audio clarity, annotation consistency, and compliance with project guidelines. The successful candidate will help ensure that all approved audio data contributes effectively to the development of advanced multilingual AI speech technologies.


Key Responsibilities

  • Review and evaluate English (US) audio recordings for quality, pronunciation accuracy, clarity, and natural speech delivery.
  • Verify annotation accuracy and ensure all submitted recordings comply with project guidelines and quality standards.
  • Identify audio quality issues including background noise, recording inconsistencies, pronunciation errors, or technical defects.
  • Provide structured quality feedback and recommend improvements when recordings do not meet required standards.
  • Ensure consistency across annotated datasets by following established QA processes and evaluation criteria.
  • Collaborate with project reviewers and annotation teams to maintain high-quality multilingual datasets.
  • Maintain accurate documentation of review outcomes and quality assurance findings.
  • Support the continuous improvement of AI speech recognition models through high-quality audio validation.


Candidate Requirements

  • Native-level fluency in English (US) with excellent listening comprehension and pronunciation knowledge.
  • Minimum 1 year of experience in audio quality assurance, audio annotation, localization, transcription review, voice-over, dubbing, ADR, or related language quality roles.
  • Strong attention to detail with the ability to identify pronunciation, fluency, and audio quality issues accurately.
  • Excellent understanding of American English linguistic nuances, regional accents, grammar, and natural speech patterns.
  • Experience reviewing audio recordings and applying quality standards consistently.
  • Familiarity with audio editing or audio playback software is preferred.
  • Strong analytical skills with the ability to provide clear and actionable quality feedback.
  • Ability to work independently while meeting project deadlines and quality targets.


Job Code: #784