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Llm Annotation Jobs (NOW HIRING)

Crystal Equation Corporation

Linguist III

Burlingame, CA · On-site

$43 - $48/hr

We are seeking candidates with strong linguistic data analysis and language technology experience to manage data collection, LLM-powered data synthesis and data annotation tasks, prompt engineering ...

Sourceability

Principal NLP Scientist

Austin, TX · On-site

Review existing models, datasets, annotation processes, and production use cases * Identify the highest-impact opportunities for NLP and LLM improvements * Define practical evaluation metrics for ...

Prolim Global

Facebook - Linguist III

Burlingame, CA · On-site

We are seeking candidates with strong linguistic data analysis and language technology experience to manage data collection, LLM-powered data synthesis and data annotation tasks, prompt engineering ...

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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 Sep 11, 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 September 2026, with employment types broken down into 1% Internship, 95% Full Time, 1% Part Time, and 3% Contract. Highlights an 74% Physical, 5% Hybrid, and 21% Remote job distribution, with an average salary of $40,000 per year, or $19.2 per hour.

AI/ML Engineer - NLP Scientist

South San Francisco, CA • On-site

Dawar Consulting, Inc.
IT Services • 11 - 50 employees
Learn more about Dawar Consulting, Inc.external

$80 - $85/hr

Other

Medical, Retirement

Posted 10 days ago

Job description

South San Francisco, United States | Posted on 08/26/2026

Our client, a world leader in biotechnology and life sciences, is looking for a “ Senior AI/ML Engineer - NLP Scientist ”.

Job Duration: Long-Term Contract (Possibility Of Extension)

Rate: $80-$85/hr on W2

Company Benefits: Medical, Paid Sick Leave, 401 (k)

We are seeking a Senior AI/ML Engineer to build an evidence-grounded AI capability that verifies generated claims against approved scientific, clinical, regulatory, and reference materials before human review. The system will retrieve relevant evidence, decompose claims into verifiable assertions, evaluate evidence support, and provide traceable decisions with citations . The system must recognize unsupported or contradicted claims and abstain rather than guess .

Key Responsibilities

Build production-grade Python/NLP pipelines for claim verification and evidence attribution.

Develop hybrid retrieval using lexical and vector search to identify relevant evidence.

Implement claim decomposition, natural language inference (NLI), entailment, and contradiction detection .

Evaluate whether generated claims are genuinely supported by cited evidence.

Design confidence thresholds, abstention logic, escalation rules, and human-in-the-loop workflows .

Build evaluation datasets with expert annotation guidelines and measure inter-annotator agreement .

Track false approvals, false rejections, abstentions, and other error categories.

Develop traceable systems that allow decisions to be reconstructed based on model version, evidence, citations, and reviewer actions .

Work with Medical, Legal, Regulatory, and scientific stakeholders to translate reviewrequirements into technical solutions.

Required Skills

NLP / LLM / Generative AI development.

RAG, hybrid search, vector search, and lexical retrieval .

Claim decomposition and evidence attribution .

LLM/model APIs and production evaluation frameworks.

AI/ML evaluation, benchmarking, and error analysis .

Human-in-the-loop AI, confidence scoring and abstention .

Experience with scientific, technical, regulatory, legal, or other high-stakes content.

Experience creating expert-labeled datasets and annotation guidelines .

Strong understanding of traceability, citations, and reproducible AI decisions .

Preferred Skills

Knowledge graphs and relationships between claims, evidence, references, products, and indications.

Deterministic rules + ML/LLM decision systems .

Pharmaceutical, biotech, healthcare, regulatory, legal, financial compliance, or scientific

publishing experience.

Familiarity with clinical studies, statistics, scientific literature, and citation practices .

Experience with LangChain, LlamaIndex, Hugging Face, PyTorch, or similar NLP/

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