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Remote Ai Tagging Jobs (NOW HIRING)

Get to Know Us Horizon3.ai is a fast-growing, remote cybersecurity company dedicated to the mission ... Partner with Engineering infra and product teams to define tagging taxonomy, identify tagging gaps ...

AI ML Engineer Location: Mason, OH (Remote) Job Type: Fulltime Must Have Technical/Functional ... Tagging and labeling workflows * Generative AI & LLMs * Prompt engineering for LLM-based ...

ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and ... Digital Tagging & Tracking * Support implementation and QA of tracking tags across media platforms ...

Buying Assistant

$20 - $27/hr

... we offer a Remote-First Workplace for many of our jobs. SwimOutlet.com is the largest online ... Manage product classification, taxonomy, and cross-categorization, leveraging AI-assisted tagging ...

... Data & AI, Autonomous Operations & Intelligence, and Enterprise Service Management. We help ... Develop a comprehensive metadata tagging strategy mapped to ASL, environment, and repository ...

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Remote Ai Tagging information

What is remote AI tagging?

Remote AI tagging is the process of labeling or annotating data, such as images, videos, or text, from a remote location to help train artificial intelligence systems. Individuals working in remote AI tagging use specialized software to identify and categorize objects, features, or patterns within data sets. This work is essential for improving the accuracy and performance of machine learning models. It can be done from anywhere with an internet connection, making it a flexible job option.

What are the key skills and qualifications needed to thrive as a Remote AI Tagging Specialist, and why are they important?

To thrive as a Remote AI Tagging Specialist, you need strong attention to detail, data annotation experience, and a basic understanding of machine learning concepts, usually backed by a relevant degree or equivalent experience. Familiarity with annotation platforms, data labeling tools, and quality control systems is typically required. Excellent communication, time management, and the ability to follow precise guidelines are crucial soft skills for excelling in this remote role. These skills ensure accurate data labeling, which is essential for developing effective AI models and maintaining project quality standards.

What is the difference between Remote Ai Tagging vs Data Labeler?

AspectRemote Ai TaggingData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexible hoursRemote or on-site, flexible hours
Industry UsageAI, machine learning, tech companiesData management, research, tech companies
Search & Comparison IntentUnderstanding AI-specific tagging rolesGeneral data labeling roles

Remote Ai Tagging and Data Labeler roles both involve labeling data for machine learning, often remotely and with similar skills. However, Remote Ai Tagging typically emphasizes AI-specific tasks like image, video, or text annotation for AI models, while Data Labelers may work on broader data categorization projects. Both roles are essential in AI development, but Remote Ai Tagging often requires familiarity with AI workflows and tools.

What are some typical challenges faced by professionals working in remote AI tagging roles, and how can they be managed?

Remote AI tagging professionals often encounter challenges such as maintaining high accuracy while labeling large volumes of data and managing repetitive tasks that require close attention to detail. Working remotely can also lead to feelings of isolation, so regular communication with team members and participating in virtual meetings can help. Establishing a structured daily routine, using productivity tools, and staying updated on annotation guidelines are effective strategies to ensure consistent quality and meet project deadlines.
More about Remote Ai Tagging jobs
What cities are hiring for Remote Ai Tagging jobs? Cities with the most Remote Ai Tagging job openings:
What are the most commonly searched types of Ai Tagging jobs? The most popular types of Ai Tagging jobs are:
What states have the most Remote Ai Tagging jobs? States with the most job openings for Remote Ai Tagging jobs include:
Infographic showing various Remote Ai Tagging job openings in the United States as of May 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.
Generative AI Data Analyst - USA (Remote)

Generative AI Data Analyst - USA (Remote)

Welo Data

Charleston, WV • Remote

$27.38/hr

Full-time

Posted 2 days ago


Job description

OVERVIEW

We are seeking a Generative AI Analyst to support a high-impact machine learning project. This role focuses on creating high-quality prompts and responses across diverse topics and leading labeling initiatives with internal and external partners. The ideal candidate is a strong communicator with native-level U.S. English, experienced in working with data and comfortable training teams on best practices for LLM development. This position is fully remote and suited for someone motivated to work with cutting-edge AI technologies.

Project Details

Job Title: Generative AI Analyst
Location: Remote
Hours: 40 hours weekly
Language: English (US)
Start date: April 2026
Employment Type: Full-time W-2 employee with benefits – 5 days a week
Pay rate: $27.38/hour
If you reside in California, please apply to the California-specific posting for the applicable rate

Must have valid work authorization in the US (Welo Data does not sponsor VISAs at this time).

Key Responsibilities
  • Creatively writing prompts and responses to a variety of diverse topics
  • Perform LLM annotation and evaluation tasks (ranking, scoring, labeling, tagging)
  • Evaluate model outputs for accuracy, relevance, and instruction-following
  • Identify and document issues such as hallucinations and inconsistencies
  • Participating in and/or supporting labeling workflows, including hands-on annotation and collaboration with internal or external teams
  • Training teams on best practices for creating Large Language Models/Data sets
Requirements
  • Hands-on experience performing data annotation or evaluation tasks (e.g., labeling, ranking, scoring, or tagging LLM outputs)
  • Native or near-native English with excellent writing skills
  • Strong attention to detail and ability to follow guidelines consistently
  • Self-driven, motivated and enthusiastic to work on state-of-art machine learning tools
  • 4 year Accredited College degree or equivalent experience

Ways to stand out from the crowd:

  • College Degree or experience in Linguistics, English Literature, Creative Writing, Journalism, and domain knowledge (Law/Medical/Math/Coding/etc.)
  • Experience working in annotation platforms or structured labeling environments is a plus
  • Deep understanding of Large Language Models/RLHF
  • Experience in labeling/tagging of frames/tasks/prompts to prepare for DNN
  • QA/testing experience

Please note that in order to verify work authorization as is required by Federal law (I-9 process), all new employees must complete a live video verification with their selected IDs and provide photos of these selected IDs within their first 3 days of employment.


Working at Welo Data

What to expect from working at Welo Data

From Welo Data

About Welo Data, in their own words

From Welo Data

Welo Data is a global AI data services company powering the next generation of AI. We build, annotate, and validate the training datasets that make AI models accurate, safe, and ready for the real world — across languages, cultures, and domains.

Our team of experts spans the globe, combining deep technical knowledge with a human-centered approach. If you want your work to shape how AI understands the world, you'll find your place here.

Diversity and inclusion statement

From Welo Data

Our Strength is derived from Winning Together. Welo Data is unequivocally committed to developing and fostering a workplace and organizational culture that values the diversity of thought and perspective delivered by a diverse global workforce operating within an inclusive organization.