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Language Annotator Jobs (NOW HIRING)

RWS Group

Data Annotator for AI Models (Italian)

$56 - $72.75/hr

Responsibilities : • Annotate data accurately and consistently according to predefined guidelines in the required language. • Perform basic research as needed to ensure accurate annotation. • ...

Turing

Client Director, Frontier Data - US

Palo Alto, CA · On-site

$255K - $325K/yr

... rate, inter-annotator agreement, and pairwise preference scoring) Client Partnership ... Experience evaluating large language model performance and/or improving model performance via fine ...

Welo Data

Quality Analytics Lead

Charleston, WV · Remote

... language models and voice and speech systems to agentic workflows and robotics and embodied AI ... annotator/rater performance, and program-level quality health. Use Python for higher-order data ...

Welo Global

Quality Analytics Lead

... language models and voice and speech systems to agentic workflows and robotics and embodied AI ... annotator/rater performance, and program-level quality health. * Use Python for higher-order data ...

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Language Annotator information

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$32K

$44.1K

$51K

How much do language annotator jobs pay per year?

As of Jul 11, 2026, the average yearly pay for language annotator in the United States is $44,079.00, according to ZipRecruiter salary data. Most workers in this role earn between $39,500.00 and $50,000.00 per year, depending on experience, location, and employer.

What does a language annotator do?

A language annotator labels and tags text data to help improve natural language processing systems. They analyze language features such as syntax, semantics, and context, often using specialized tools and following detailed guidelines. This work supports the development of AI models, speech recognition, and machine translation applications.

What are Language Annotators?

Language Annotators are professionals who label, categorize, and tag text, audio, or speech data to help train and improve natural language processing systems and AI models. Their work involves identifying linguistic features such as parts of speech, named entities, sentiment, or intent in language data. Language Annotators play a crucial role in making AI technologies like chatbots, translation tools, and voice assistants more accurate and effective. They often work with large datasets and follow specific guidelines to ensure consistency and quality in the annotations.

What are the key skills and qualifications needed to thrive as a Language Annotator, and why are they important?

To thrive as a Language Annotator, you need strong linguistic knowledge, attention to detail, and typically a background in linguistics or a related field. Familiarity with annotation tools, text analysis software, and version control systems like Git is often required. Excellent communication, critical thinking, and the ability to follow detailed guidelines are essential soft skills. These skills ensure the production of high-quality, consistent data crucial for training effective language models and supporting NLP research.

Is linguistics in high demand?

Linguistics-related roles, including language annotators, are increasingly in demand due to growth in natural language processing, machine learning, and AI technologies. Skills in language analysis, annotation tools, and understanding of linguistic structures enhance employability in this field.

What are some common challenges faced by Language Annotators, and how can they be managed effectively?

Language Annotators often encounter challenges such as maintaining consistency in annotation, managing large volumes of data, and adapting to evolving guidelines. To address these, it's important to communicate regularly with team members, participate in calibration sessions, and seek clarification when guidelines are unclear. Utilizing annotation tools efficiently and staying organized can also help manage workload and ensure high-quality results.

How much do AI annotators make?

AI annotators typically earn between $12 and $20 per hour, depending on experience, location, and the complexity of the annotation tasks. Many positions are freelance or part-time, often requiring attention to detail and familiarity with annotation tools.

What qualifications do you need to be a data annotator?

To be a language annotator, candidates typically need strong language skills, attention to detail, and basic computer proficiency. Some roles may require familiarity with annotation tools or specific training, but formal certifications are not usually mandatory.
More about Language Annotator jobs
What job categories do people searching Language Annotator jobs look for? The top searched job categories for Language Annotator jobs are:
Language Annotator jobs near you
Infographic showing various Language Annotator job openings in the United States as of July 2026, with employment types broken down into 6% Internship, 74% As Needed, 1% Full Time, 1% Contract, 16% Nights, and 2% Summer. Highlights an 39% Physical, 1% Hybrid, and 60% Remote job distribution, with an average salary of $44,079 per year, or $21.2 per hour.
Applied Data Scientist, Health AI Evaluation & Datasets

Applied Data Scientist, Health AI Evaluation & Datasets

Innodata Inc.

Remote

Full-time

Posted 5 days ago

Innodata rating

7.3

Company rating: 7.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

149th of 209 rated software companies

Job description

Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale. We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.
Scope of the Role:
Healthcare is one of the highest-stakes domains for generative AI. Clinical accuracy, patient safety, regulatory compliance, health equity, auditability, and workflow fit are the bar for shipping anything real. Innodata partners with foundation model labs, medical AI startups, payers, providers, pharma, and digital health companies building LLMs, multimodal systems, and AI agents for healthcare and life sciences.
As an Applied Data Scientist, Health AI Evaluation & Datasets, you own the design, measurement quality, and clinical validity of datasets used to train, fine-tune, and evaluate health-domain models. You bring clinical or biomedical fluency and data science rigor: you can read a clinical guideline, payer policy, medical literature artifact, or patient communication workflow; translate it into a measurable dataset and evaluation plan; and defend the methodology to sophisticated clinical, data science, and ML stakeholders.
You will work in a tight pod with a Technical Solutions Architect, Applied Research Scientist, AI/ML Research Engineer, and Language Data Scientists. Your role is to make sure the data, rubrics, review workflows, and measurement evidence are clinically realistic, statistically defensible, compliant, and useful for evaluation and post-training.
What You'll Own:
  • Translate customer goals - such as improving differential diagnosis, evaluating a clinical note summarizer, testing a RAG-based medical literature assistant, or creating preference data for patient-facing chatbots - into dataset specifications, taxonomies, rubrics, sampling plans, and acceptance criteria.
  • Make multimodal health AI a core focus: design training and evaluation datasets across clinical text, medical images, waveforms, structured EHR data, claims, trial data, medical literature, patient communications, payer policies, drug information, and other clinical artifacts, as well as use cases such as clinical reasoning, medical QA, note summarization, medical coding, patient communication, utilization management, and literature synthesis.
  • Design evaluations for retrieval-augmented and source-grounded health AI systems, including evidence citation, faithfulness, contraindication handling, guideline adherence, source freshness, and failure modes caused by incomplete, conflicting, or stale context.
  • Define sampling strategies, label schemas, inter-annotator agreement targets, adjudication workflows, SME review patterns, and quality thresholds in partnership with Language Data Scientists, clinicians, biomedical experts, and quality teams.
  • Build statistical and ML checks that make healthcare datasets trustworthy: stratified sampling across specialties and patient subgroups, bias and representation analysis, leakage detection, distribution shift checks, uncertainty estimates, reliability metrics, and subgroup performance analysis.
  • Partner with Applied Research Scientists and AI/ML Research Engineers to instrument datasets into evaluation and post-training pipelines, including rubric-grounded LLM-as-judge prompts, regression suites, model comparison workflows, experiment tracking, and model-improvement feedback loops.
  • Evaluate health AI behavior beyond surface accuracy: calibration, hallucination on safety-critical content, refusal appropriateness, robustness under ambiguity, equity across patient subgroups, and safe handoff in agentic or workflow-integrated systems. Reason concretely about clinical workflow fit: where outputs enter care delivery, what evidence a clinician or reviewer would need to trust them, when uncertainty must be surfaced, and how patient-facing, clinician-facing, payer, pharma, and operational use cases differ in risk.
  • Own data quality from source intake through delivery, including de-identified clinical text, medical literature, synthetic cases, structured records, client policies, and knowledge bases, with attention to PHI/PII handling, provenance, audit trails, versioning, and compliance documentation.
  • Stay current on the health AI landscape - regulatory developments such as FDA guidance on AI/ML-enabled medical devices and EU AI Act health provisions, benchmark releases such as MedQA, MedMCQA, and HealthBench, and emerging clinical evaluation methodology.
  • Support customer discovery and proposal work by scoping dataset programs, sizing annotation and SME review effort, identifying regulatory or data-access constraints, and explaining methodology choices to client clinical and ML leadership.
  • Contribute to Innodata internal IP: reusable health-domain taxonomies, evaluation rubrics, golden datasets, clinical review playbooks, dataset quality checks, and methodology templates.

You'll Thrive in This Role If You Have:
  • 5+ years of data science experience, including at least 2+ years with healthcare, clinical, biomedical, payer, provider, pharma, life sciences, or comparable regulated health data.
  • Working knowledge of healthcare data and standards: EHR structure, clinical documentation conventions, ICD-10, CPT, SNOMED CT, LOINC, RxNorm, and at least passing familiarity with FHIR, HL7, or equivalent interoperability concepts.
  • Hands-on experience designing ML datasets, not just consuming them: writing annotation guidelines, sizing cohorts, setting quality thresholds, designing QA checks, and shipping data that downstream teams can train or evaluate on.
  • Familiarity with LLM-based health AI workflows, including prompt design, rubric-based evaluation, retrieval-augmented generation, LLM-as-judge methods, model comparison, and the limitations of automated evaluation in clinical contexts.
  • Strong Python and SQL; comfort with pandas, scikit-learn, statsmodels or equivalent tools; and working familiarity with modern LLM tooling such as Hugging Face, evaluation frameworks, prompt development tools, or model APIs.
  • Statistical literacy across sampling design, bias and fairness analysis, inter-annotator agreement metrics (Cohen or Fleiss kappa, Krippendorff alpha), confidence intervals, significance testing where appropriate, error analysis, and the ability to push back when a number is being over-interpreted.
  • Solid grasp of healthcare privacy, compliance, and governance: HIPAA, de-identification standards (Safe Harbor and Expert Determination), practical mechanics of working with PHI safely, auditability, access control, and documentation fit for high-stakes or regulated AI programs.
  • Ability to work credibly with clinicians, biomedical SMEs, research scientists, engineers, technical solutions teams, annotators, and customer stakeholders.
  • A bias toward clinical realism: you would rather build a smaller dataset that reflects what clinicians, reviewers, patients, or care teams actually see than a larger dataset that looks impressive on paper but fails in practice.
  • Degree in a relevant field such as biostatistics, epidemiology, computational biology, health informatics, computer science with a health focus, statistics, a clinical degree with quantitative training, or equivalent demonstrated experience.
  • Clinical credentials are not required, but candidates must be able to work credibly with clinicians, biomedical SMEs, and health AI customers; candidates with MD, RN, PharmD, MPH, PhD, or health informatics backgrounds are especially encouraged.

The expected salary range for this position is $150,000 - $175,000 USD per year, based on experience, skills, and qualifications.
Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission's guide at https://consumer.ftc.gov/articles/job-scams.
If you believe you've been targeted by a recruitment scam, please report it to Innodata at verifyjoboffer@innodata.com and consider reporting it to the FTC at ReportFraud.ftc.gov.

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