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Llm Annotation Jobs (NOW HIRING)

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... 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 ...

Cyborg Mobile

Data & AI Program Manager

Redmond, WA · On-site +1

Strong understanding of data annotation workflows, guideline design, and inter-annotator quality control * Familiarity with LLM training/fine-tuning pipelines and how annotated data feeds model ...

New

Cyborg Mobile

Data & AI Program Manager

Redmond, WA · On-site +1

Strong understanding of data annotation workflows, guideline design, and inter-annotator quality control * Familiarity with LLM training/fine-tuning pipelines and how annotated data feeds model ...

New

Cerebras

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... 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 ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... 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 ...

Apple Inc.

... LLM-as-a-Judge. * Experience with Human-in-the-Loop evaluation, annotation, or data-quality workflows. * Strong understanding of statistical analysis, experimentation, sampling, and measurement ...

New

Handshake

AI Red Teamer (LLM Generalist)

Seattle, WA · On-site

$32 - $95/hr

AI Red Teamer (LLM Generalist) Location: Seattle, WA (candidates must reside in the Seattle metro ... Comfort with structured data annotation and rubric-based scoring * Prior work in trust and safety ...

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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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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.

Client Director, Frontier Data - US

Palo Alto, CA • On-site

Turing
IT Services • 51 - 200 employees
Learn more about Turingexternal

$255K - $325K/yr

Full-time

Re-posted 16 days ago

Job description

Overview

We are seeking a seasoned techno-functional leader to drive the development and execution of large-scale LLM training programs. This leader would partner with our clients (leading LLM labs) research teams to:

  • Identify opportunities for building training datasets to improve model capabilities and performance
  • Generate these datasets with high quality and speed
  • Build automation tools and processes for scalability
  • Deliver the datasets so that they are easily usable by our clients

Key Responsibilities

Operational Leadership & Performance Management

  • Lead and scale global delivery teams of 100+, distributed across functions, regions, and levels (ICs, leads, and managers)
  • Implement performance management systems that go beyond managerial reporting using data-driven metrics, tools, and products to assess productivity, quality, and output consistency
  • Build strong operational structures that allow for transparency, accountability, and early detection of underperformance
  • Partner with cross-functional leads to optimize workflows and improve internal tool adoption for delivery efficiency

Data Quality & Scripting-Driven Automation

  • Own the quality, accuracy, and scalability of data generated for LLM training
  • Move beyond manual QA layers by leveraging Python scripting, APIs, and automation frameworks to measure, validate, and improve dataset integrity
  • Design and oversee tools or scripts for data validation, 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 & Evaluation

  • Lead generation and delivery of high-quality, scalable datasets focused on SFT, RLHF, reasoning, and agentic workflows
  • Oversee the entire data lifecycle from client intake and annotation workflow design to delivery
  • Partner with product, research, and engineering teams to implement evaluation metrics (e.g., win rate, inter-annotator agreement, and pairwise preference scoring)

Client Partnership & Communication

  • Serve as the primary point of contact for enterprise AI clients; manage expectations, delivery timelines, and escalations
  • Build relationships with engineering and research stakeholders by delivering consistently high-quality data
  • Communicate effectively across technical and non-technical audiences; provide transparency through structured updates and quality reporting

Team Development & Tooling

  • Recruit, mentor, and coach cross-functional leaders (Eng, Data, Ops, and Program Management)
  • Drive adoption and improvement of internal tools (e.g., task management systems, quality dashboards)
  • Champion continuous improvement across data quality, tools, and delivery processes

Required Qualifications

  • 10+ years of experience leading large-scale technical delivery organizations, ideally across AI, ML, or data operations
  • Bachelor's degree in Engineering, Computer Science, or equivalent technical discipline
  • Demonstrated ability to act as a strategic business partner with our clients, researchers, and engineers at leading LLM labs
  • Proven success in building and scaling multi-level high performance teams, with distributed global operations
    • Experience managing managers
    • Skip-level performance management
  • Hands-on technical fluency: ability to write and review data validation scripts
  • Demonstrated experience managing dataset generation or annotation for machine learning model evaluation and/or training 
  • Familiarity with ML tools and data workflows (e.g., HuggingFace, LangChain, Weights & Biases, Databricks)

Preferred Qualifications

  • Experience evaluating large language model performance and/or improving model performance via fine-tuning
  • Strong understanding of data quality frameworks, including automation, toolings and manual processes 
  • Experience in AI data annotation, model evaluation, and fine-tuning platforms
  • Strong communication and storytelling skills with executive stakeholders

Location SF Bay Area (Hybrid)
Compensation: $255,000 to $325,000 OTE + Equity

Apply