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Ai Llm Data Labeling Jobs in Florida (NOW HIRING)

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

TSMG

AI/ML Data Contributor

Miami, FL · On-site

Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation * Participate in remote assignments or attend on-site sessions when required * Follow project guidelines and ...

MethodHub

Data Annotator

Tampa, FL · On-site

Experience working with data annotation and machine learning pipeline management platforms and tools such as Label Studio, CVAT, Prodigy, Amazon SageMaker Ground Truth * Basic understanding of AI/ML ...

New

Rempli

AI Product Manager

Miami, FL · On-site

$130K - $160K/yr

Prioritize features based on impact, feasibility, and data * Measure product performance and ... AI/LLM-powered features * Agile product development * User research and experimentation

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Ai Llm Data Labeling information

What is AI LLM data labeling?

AI LLM data labeling is the process of annotating or tagging data—such as text, images, or audio—to provide clear examples that help train large language models (LLMs) like GPT or BERT. This labeled data is essential for teaching models to understand context, intent, and meaning, which improves their performance on various tasks. Data labelers often follow specific guidelines to ensure consistency and accuracy, making their role critical in developing reliable AI systems.

What are some common challenges faced in AI LLM data labeling and how can they be managed?

One common challenge in AI LLM data labeling is ensuring consistency and accuracy when annotating large volumes of complex language data. Labelers often encounter ambiguous or context-dependent text, making it important to follow detailed guidelines and participate in regular calibration sessions with the team. Collaboration with data scientists and project managers is essential to clarify edge cases and refine labeling criteria. Proactively communicating questions and feedback helps maintain high-quality datasets, which are critical for training reliable language models.

What are the key skills and qualifications needed to thrive as an AI LLM data labeling specialist, and why are they important?

To thrive as an AI LLM Data Labeling Specialist, you need keen attention to detail, strong analytical skills, and a foundational understanding of natural language processing concepts, often supported by familiarity with data annotation guidelines. Experience with labeling platforms (such as Labelbox or Prodigy), spreadsheet tools, and sometimes proficiency in scripting languages like Python is highly valued. Excellent communication, consistency, and critical thinking are crucial soft skills for interpreting ambiguous data and ensuring labeling accuracy. These skills and qualifications are vital for producing high-quality training data that directly impacts the performance and reliability of large language models.

What is the difference between Ai Llm Data Labeling vs Data Annotation Specialist?

AspectAi Llm Data LabelingData Annotation Specialist
CredentialsBasic technical skills, familiarity with labeling toolsSimilar technical skills, often with additional domain knowledge
Work EnvironmentData labeling platforms, remote or office settingsData annotation projects, remote or onsite
Industry UsageAI, machine learning, NLP projectsData preparation across various industries including AI

Ai Llm Data Labeling and Data Annotation Specialist roles both involve preparing data for machine learning models. However, Ai Llm Data Labeling typically focuses on labeling data specifically for large language models, requiring familiarity with NLP and AI tools. Data Annotation Specialists may work across broader data types and industries, with a focus on accurate data tagging. Both roles demand similar skills but differ in scope and application within AI projects.

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Ai Llm Data Labeling jobs near you

AI/ML Data Contributor

TSMG

Miami, FL • On-site

Full-time

Re-posted 15 days ago

Job description

Project Overview
We are currently hiring AI/ML Data Contributors to support a range of active and upcoming projects across the United States. In this role, you will participate in tasks that help improve machine learning models, including data labeling, content evaluation, and user-based testing.

Projects may vary in scope and format, offering both remote and in-person opportunities (such as device or VR testing). This is a flexible, task-based role with the opportunity to participate in multiple projects over time.

Responsibilities
  • Perform AI/ML-related tasks such as data labeling, annotation, and content evaluation
  • Participate in remote assignments or attend on-site sessions when required
  • Follow project guidelines and ensure high-quality task completion
  • Provide feedback and input during testing activities
  • Complete tasks within given timelines
Requirements
  • Must be based in the United States
  • Strong attention to detail and ability to follow instructions
  • Basic computer skills and familiarity with digital tools
  • Reliable internet connection and access to a computer or smartphone
  • Availability to participate in task-based work (schedule may vary)
Nice to Have
  • Previous experience in data annotation, QA, or testing
  • Interest in AI, machine learning, or emerging technologies
What We Offer
  • Paid, flexible task-based work
  • Opportunity to work on innovative AI/ML projects
  • Exposure to cutting-edge technologies (including device and VR testing)
  • Potential for ongoing project participation
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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