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Llm Annotation Jobs (NOW HIRING)

ESR Healthcare

LLM Red-Teamer

Manhattan, NY · On-site

LLM Red-Teamer Role Type: Contractor Location: Remote Scope of Work Develop complex, adversarial ... Prior experience in AI human data environments (RLHF, SFT, evaluations, annotation, or prompt ...

ClifyX

Python GenAI Model Evaluator

Austin, TX · On-site

PyTorch, TensorFlow, HuggingFace, LangChain. • Evaluation/annotation tools: Scale AI, GroundTruth, Labelbox, Prodigy. • Prompt testing tools: Weights & Biases, MLflow, OpenAI evals, LLM-as-a ...

Handshake

AI Red Teamer (LLM Generalist)

Seattle, WA · On-site

... data annotation and rubric-based scoring • Prior work in trust and safety, content moderation, QA, or security research • Subject matter expertise in any high-risk domain (cybersecurity ...

Cerebras

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255 - $325/hr

... annotation accuracy checks, and pipeline consistency * Ensure datasets adhere to compliance standards (PII, GDPR, HIPAA) and can be programmatically tested for usability and quality LLM Training ...

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Llm Annotation information

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$11K

$41.5K

How much do llm annotation jobs pay per year?

As of Aug 22, 2026, the average yearly pay for llm annotation in the United States is $40,000.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,000.00 and $40,000.00 per year, depending on experience, location, and employer.

What is LLM annotation?

LLM annotation refers to the process of labeling or tagging data specifically for training and evaluating large language models (LLMs) like GPT or BERT. Annotators read text and apply labels, correct errors, or provide feedback to help improve the model's understanding and performance. This work is crucial for supervised learning, as well-annotated datasets help LLMs better recognize patterns, context, and meaning in human language. LLM annotation can involve tasks such as sentiment analysis, named entity recognition, or instruction following. Annotators often use specialized platforms or tools to complete their tasks efficiently and accurately.

What are the key skills and qualifications needed to thrive as an LLM annotation specialist?

To thrive as an LLM Annotation Specialist, you need strong analytical skills, attention to detail, and a background in linguistics, computer science, or a related field. Familiarity with annotation platforms, natural language processing (NLP) tools, and data labeling systems is typically required. Excellent communication, critical thinking, and the ability to follow guidelines precisely are valuable soft skills for this role. These skills ensure high-quality, accurate data annotation, which directly impacts the performance and reliability of large language models.

What are some common challenges faced by LLM annotation specialists, and how can they be addressed?

LLM Annotation specialists often encounter challenges such as interpreting ambiguous language data, maintaining annotation consistency across complex datasets, and keeping up with evolving guidelines. These can be addressed by participating in regular team syncs to clarify guidelines, using annotation tools with built-in quality checks, and collaborating closely with project leads and fellow annotators. Continuous learning and open communication help ensure high-quality, reliable data annotation and support professional growth within the AI and NLP fields.

What is the difference between Llm Annotation vs Data Labeler?

AspectLlm AnnotationData Labeler
Required CredentialsBasic computer skills, sometimes familiarity with AI toolsBasic skills, often on-the-job training
Work EnvironmentRemote or office-based, tech-focusedRemote or on-site, varied industries
Industry UsageAI, machine learning, NLP projectsVarious industries including marketing, healthcare, and tech
Search & Comparison IntentUnderstanding roles in AI data preparationGeneral data labeling tasks

In summary, Llm Annotation involves specialized annotation for large language models, often requiring familiarity with AI tools, while Data Labeler is a broader role focused on labeling data across multiple industries with minimal technical requirements.

How to become an Llm annotator?

To become an LLM annotator, candidates typically need strong language skills, attention to detail, and familiarity with data annotation tools. Many positions require a high school diploma or equivalent, and some companies provide training. Experience with machine learning or natural language processing can be beneficial but is not always necessary.
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Llm Annotation jobs near you
Infographic showing various Llm Annotation job openings in the United States as of August 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 77% Physical, 6% Hybrid, and 17% Remote job distribution, with an average salary of $40,000 per year, or $19.2 per hour.

Generative AI Data Analyst - USA (Remote)

Welo Data

Charleston, WV • Remote

$27.38/hr

Full-time

Re-posted 16 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.