... inter-annotator consistency, and preference modeling. • Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio) • Technical fluency in data ...
... inter-annotator consistency, and preference modeling. • Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio) • Technical fluency in data ...
... inter-annotator consistency, and preference modeling. • Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio) • Technical fluency in data ...
... inter-annotator consistency, and preference modeling. • Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio) • Technical fluency in data ...
Technical Solutions Architect, Evals & Fine-Tuning
$140K - $160K/yr
... eval (text, image, audio, video, long-context). * Architect engagements that combine Innodata ... inter-annotator agreement, and human eval workflow design. * Strong fluency in Python and the ...
Technical Solutions Architect, Evals & Fine-Tuning
$140K - $160K/yr
... eval (text, image, audio, video, long-context). * Architect engagements that combine Innodata ... inter-annotator agreement, and human eval workflow design. * Strong fluency in Python and the ...
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
$150 - $200/hr
... text: information extraction, NER, classification, sequence labelling, weak supervision, and the evaluation practice around them, including annotation guidelines and inter-annotator agreement you've ...
New
$150 - $200/hr
... text: information extraction, NER, classification, sequence labelling, weak supervision, and the evaluation practice around them, including annotation guidelines and inter-annotator agreement you've ...
New
... text, medical images, waveforms, structured EHR data, claims, trial data, medical literature ... Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication ...
... text, medical images, waveforms, structured EHR data, claims, trial data, medical literature ... Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication ...
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
Research Engineer - Data Quality & Evals
San Francisco, CA · On-site
$134K - $162K/yr
Design evaluation methodology for report generation that goes beyond surface-level text overlap ... inter-annotator agreement * Experience with clinical accuracy metrics for report generation (e.g ...
Research Engineer - Data Quality & Evals
San Francisco, CA · On-site
$134K - $162K/yr
Design evaluation methodology for report generation that goes beyond surface-level text overlap ... inter-annotator agreement * Experience with clinical accuracy metrics for report generation (e.g ...
... text, medical images, waveforms, structured EHR data, claims, trial data, medical literature ... Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication ...
... text, medical images, waveforms, structured EHR data, claims, trial data, medical literature ... Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication ...
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines. * Producing concise summaries of longer pieces of text or data.
Senior Language Engineer
San Francisco, CA · On-site
$200K - $290K/yr
... text and audio modalities. * Engineer web-scale data pipelines and apply synthetic generation ... A annotator output. * Build data processing and cleaning pipelines that align datasets to ...
Senior Language Engineer
San Francisco, CA · On-site
$200K - $290K/yr
... text and audio modalities. * Engineer web-scale data pipelines and apply synthetic generation ... A annotator output. * Build data processing and cleaning pipelines that align datasets to ...
WV · On-site
$200K/yr
Establish gold standard datasets, inter-annotator agreement (IAA) targets, and audit sampling ... Familiarity with common ML labeling tasks: text classification, NER, document extraction, intent ...
WV · On-site
$200K/yr
Establish gold standard datasets, inter-annotator agreement (IAA) targets, and audit sampling ... Familiarity with common ML labeling tasks: text classification, NER, document extraction, intent ...
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
Define and track SLAs across throughput, inter-annotator agreement, cost per label, and data ... Experience evaluating 3D, games, videos, or multimodal data beyond standard text and image ...
Define and track SLAs across throughput, inter-annotator agreement, cost per label, and data ... Experience evaluating 3D, games, videos, or multimodal data beyond standard text and image ...
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. * Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. Operations Scaling:
... text, analyzing multimedia content, contributing voice recordings, among others. You will ... Experience as AI reviewer, annotator, or evaluator preferred * Comfortable working with both ...
Quick apply
... text, analyzing multimedia content, contributing voice recordings, among others. You will ... Experience as AI reviewer, annotator, or evaluator preferred * Comfortable working with both ...
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. Operations Scaling:
... text, video, audio, speech, and document modalities, where off-the-shelf editors fall short and interaction design directly determines annotator throughput and error rate. Operations Scaling:
Technical Solutions Architect, Evals & Fine-Tuning
$140K - $160K/yr
... eval (text, image, audio, video, long-context). * Architect engagements that combine Innodata ... inter-annotator agreement, and human eval workflow design. * Strong fluency in Python and the ...
Technical Solutions Architect, Evals & Fine-Tuning
$140K - $160K/yr
... eval (text, image, audio, video, long-context). * Architect engagements that combine Innodata ... inter-annotator agreement, and human eval workflow design. * Strong fluency in Python and the ...
Text Annotator information
See salary details
$13.70 - $15.03
2% of jobs
$15.03 - $16.37
7% of jobs
$16.37 - $17.70
10% of jobs
$18.92 is the 25th percentile. Wages below this are outliers.
$17.70 - $19.03
6% of jobs
$19.03 - $20.37
9% of jobs
$20.37 - $21.70
7% of jobs
$21.70 - $23.03
0% of jobs
$23.03 - $24.37
1% of jobs
The median wage is $24.56 / hr.
$24.37 - $25.70
50% of jobs
$25.70 - $27.03
4% of jobs
$27.03 - $28.37
3% of jobs
$13
$22
$28
How much do text annotator jobs pay per hour?
As of Sep 9, 2026, the average hourly pay for text annotator in the United States is $22.63, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $25.00 per hour, depending on experience, location, and employer.
What is a text annotator?
Text Annotators are professionals who label and categorize text data to help train artificial intelligence (AI) and machine learning models. Their work involves reading documents, identifying specific elements such as entities, sentiments, or key phrases, and tagging these according to predefined guidelines. This process ensures that AI systems can accurately interpret and process human language by learning from well-labeled examples. Text annotators often work with a variety of texts, including emails, social media posts, and articles, to create high-quality datasets for natural language processing (NLP) applications.
What are the key skills and qualifications needed to thrive as a text annotator, and why are they important?
To thrive as a Text Annotator, you need strong language proficiency, attention to detail, and familiarity with linguistic concepts, often supported by a degree in linguistics, language studies, or a related field. Experience with annotation tools, text labeling platforms, and sometimes basic scripting or data management systems is typical. Excellent focus, time management, and clear communication are important soft skills in this role. These capabilities ensure the accurate and efficient preparation of high-quality annotated datasets essential for machine learning and natural language processing projects.
What are some common challenges faced by text annotators, and how can they be addressed?
Text annotators often encounter challenges such as maintaining consistency in labeling, understanding ambiguous language, and managing large volumes of data. To address these, teams typically provide detailed annotation guidelines, conduct regular training sessions, and use collaborative review processes to ensure accuracy. Utilizing annotation tools with built-in quality checks and establishing open communication with project managers can also help annotators overcome these hurdles and deliver high-quality results.
What is the difference between Text Annotator vs Data Labeler?
| Aspect | Text Annotator | Data Labeler |
|---|---|---|
| Credentials | Basic computer skills, attention to detail | Basic computer skills, attention to detail |
| Work Environment | Data annotation platforms, remote or office | Data labeling platforms, remote or office |
| Industry Usage | AI, machine learning, NLP projects | AI, machine learning, data preparation |
| Search Intent | Compare roles in data annotation | Compare roles in data labeling |
Text Annotators and Data Labelers often perform similar tasks in AI and machine learning projects, focusing on preparing data for model training. While the terms are sometimes used interchangeably, Text Annotators typically specialize in labeling text data specifically, such as identifying entities or sentiment, whereas Data Labelers may work with various data types, including images and audio. Both roles require attention to detail and familiarity with annotation tools, making them closely related but distinct in scope.
What are popular job titles related to Text Annotator jobs?
For Text Annotator jobs, the most frequently searched job titles are:
Jr. Strategic Project Lead, DataFactory
Remote
Ursus, Inc. •
Recruiting and Staffing Services • 11 - 50 employees11 - 50 employees
Full-time
Re-posted 11 days ago
Job description
Job Summary:
Ursus, Inc. is a global leader in AI and high-performance computing, powering innovations in gaming, robotics, and data science. They are seeking a seasoned, techno-functional leader to drive the development and execution of large-scale LLM training programs, focusing on dataset creation, annotation, and workflows.
Responsibilities:
• Lead the generation and delivery of high-quality, scalable LLM training datasets with a focus on SFT, RLHF, rubric-based evaluation, reasoning, and agentic workflows.
• Oversee the end-to-end data lifecycle from customer intake to delivery, including collaboration with the researchers for defining the data requirements, guidelines, designing the data annotation steps/workflow, quality metrics, review workflows, and delivery setup.
• Serve as the primary contact for large engagements; manage stakeholder expectations, requirements gathering, and delivery timelines.
• Collaborate with engineering, product, research, and delivery teams to ensure technical feasibility and alignment across workstreams.
• Develop, document, and refine best practices for prompt evaluation, data schema design, evaluation metrics (e.g., win rate, pairwise preference), and human-in-the-loop QA.
• Manage and mentor leads, program managers, and annotators working across multiple AI training pods.
• Operate as a strategic business partner to our customers; provide insight on tradeoffs, resourcing, and performance metrics.
• Build internal capability by harvesting reusable assets and contribute in continued improvements across data quality, tools and processes.
Qualifications:
Required:
• 10+ years of experience building and leading large scale technical delivery teams. Proven ability to lead large cross functional teams (100+) by building strong operations
• Bachelor’s degree in Engineering, Computer Science or equivalent practical experience leading large-scale technical initiatives.
• Demonstrated experience managing large-scale dataset generation or annotation for LLMs, ideally with experience in RLHF or SFT pipelines.
• Strong understanding of quality review mechanisms including prompt win rate, agreement metrics, inter-annotator consistency, and preference modeling.
• Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio)
• Technical fluency in data platforms, machine learning concepts and modern Machine Learning tooling (e.g., HuggingFace, LangChain, Weights & Biases).
• Strong communication skills with experience presenting to executive stakeholders, managing client escalations, and aligning delivery with strategic goals.
Preferred:
• Prior experience at AI Data Platform companies
• Past experience with retrieval augmented generation (RAG), fine-tuning, and human evaluation workflows.
• Familiarity with fine-tuning LLMs, prompt engineering at scale, and instruction dataset design.
• Technical fluency in Python and cloud infrastructure
• Familiarity with bench marks (SWE Bench, MMMLU, or equivalent)
Company:
Ursus, Inc., has been recognized by Staffing Industry Analysts (SIA) for four consecutive years as the fastest-growing technical and creative staffing firm in the United States. Founded in 2015, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.
Ursus, Inc. is a global leader in AI and high-performance computing, powering innovations in gaming, robotics, and data science. They are seeking a seasoned, techno-functional leader to drive the development and execution of large-scale LLM training programs, focusing on dataset creation, annotation, and workflows.
Responsibilities:
• Lead the generation and delivery of high-quality, scalable LLM training datasets with a focus on SFT, RLHF, rubric-based evaluation, reasoning, and agentic workflows.
• Oversee the end-to-end data lifecycle from customer intake to delivery, including collaboration with the researchers for defining the data requirements, guidelines, designing the data annotation steps/workflow, quality metrics, review workflows, and delivery setup.
• Serve as the primary contact for large engagements; manage stakeholder expectations, requirements gathering, and delivery timelines.
• Collaborate with engineering, product, research, and delivery teams to ensure technical feasibility and alignment across workstreams.
• Develop, document, and refine best practices for prompt evaluation, data schema design, evaluation metrics (e.g., win rate, pairwise preference), and human-in-the-loop QA.
• Manage and mentor leads, program managers, and annotators working across multiple AI training pods.
• Operate as a strategic business partner to our customers; provide insight on tradeoffs, resourcing, and performance metrics.
• Build internal capability by harvesting reusable assets and contribute in continued improvements across data quality, tools and processes.
Qualifications:
Required:
• 10+ years of experience building and leading large scale technical delivery teams. Proven ability to lead large cross functional teams (100+) by building strong operations
• Bachelor’s degree in Engineering, Computer Science or equivalent practical experience leading large-scale technical initiatives.
• Demonstrated experience managing large-scale dataset generation or annotation for LLMs, ideally with experience in RLHF or SFT pipelines.
• Strong understanding of quality review mechanisms including prompt win rate, agreement metrics, inter-annotator consistency, and preference modeling.
• Experience with human data generation across at least one of the modalities (Text, Image, Video, Audio)
• Technical fluency in data platforms, machine learning concepts and modern Machine Learning tooling (e.g., HuggingFace, LangChain, Weights & Biases).
• Strong communication skills with experience presenting to executive stakeholders, managing client escalations, and aligning delivery with strategic goals.
Preferred:
• Prior experience at AI Data Platform companies
• Past experience with retrieval augmented generation (RAG), fine-tuning, and human evaluation workflows.
• Familiarity with fine-tuning LLMs, prompt engineering at scale, and instruction dataset design.
• Technical fluency in Python and cloud infrastructure
• Familiarity with bench marks (SWE Bench, MMMLU, or equivalent)
Company:
Ursus, Inc., has been recognized by Staffing Industry Analysts (SIA) for four consecutive years as the fastest-growing technical and creative staffing firm in the United States. Founded in 2015, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.
About Ursus
Sourced by ZipRecruiter
Ursus is a recognized leader in providing technical and creative staffing solutions. Hyper - focused on you - ( the candidate, the client, the partner, the employee ) and how to make your engagement with us the best possible experience it can be while delivering results. Whether you are looking for your next career move, scaling a growing team, adding a trusted partner to your staffing program or completing a project, we’re here for U!
Industry
Recruiting and staffing services
Company size
11 - 50 Employees
Headquarters location
Morgan Hill, CA, US
Year founded
2015