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Full Time Linguistic Annotation Jobs (NOW HIRING)

Propio Language Services

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Job Type Full-time Description Propio Language Services is a provider of the highest quality ... The ideal candidate can build scalable data pipelines, design high-quality annotation and QA ...

Welocalize, Inc.

OR · On-site

$16 - $20.75/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

Welocalize, Inc.

Data Labeling Associate

New York, NY · On-site

$17.50 - $22.75/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

Welocalize, Inc.

Data Labeling Associate

San Diego, CA

$17 - $22/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

Welocalize, Inc.

OR · On-site

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

Welocalize, Inc.

OR · On-site

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

Welocalize, Inc.

Data Labeling Associate

New York, NY

$17.50 - $22.75/hr

The ideal candidate will have a foundational understanding of machine learning, data annotation ... Ensure linguistic accuracy in all processed and annotated data. REQUIREMENTS Preferred ...

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All JobsFull Time Linguistic Annotation Jobs

Full Time Linguistic Annotation information

See salary details

$45K

$58.4K

$97.5K

How much do full time linguistic annotation jobs pay per year?

As of Jun 16, 2026, the average yearly pay for full time linguistic annotation in the United States is $58,415.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $58,000.00 per year, depending on experience, location, and employer.

What are full time linguistic annotation jobs?

Full time linguistic annotation jobs involve analyzing, labeling, and categorizing language data to help train and improve natural language processing (NLP) systems, like speech recognition or machine translation. Linguistic annotators work with large datasets of text or audio, tagging elements like syntax, semantics, or sentiment according to specific guidelines. These roles require strong language skills, attention to detail, and often knowledge of linguistics or computational linguistics. Full time positions may be found at tech companies, research institutions, or linguistic data service providers.

What are the key skills and qualifications needed to thrive as a Full Time Linguistic Annotation Specialist, and why are they important?

To thrive as a Full Time Linguistic Annotation Specialist, you need a strong background in linguistics, excellent language proficiency, and attention to detail, often supported by a relevant degree or coursework. Familiarity with annotation tools, data labeling platforms, and basic scripting or database systems is typically required. Strong analytical thinking, teamwork, and clear communication skills help you effectively interpret data and collaborate with project teams. These skills are essential to ensure high-quality, consistent labeled data that supports the development of accurate natural language processing and AI models.

What are some common challenges faced by linguistic annotators in a full-time role, and how are these typically addressed within teams?

Full-time linguistic annotators often encounter challenges such as maintaining consistency across large datasets, understanding nuanced language phenomena, and keeping up with evolving annotation guidelines. Teams typically address these issues through regular training sessions, detailed documentation, and collaborative review processes. Open communication and feedback loops are also encouraged, allowing annotators to clarify ambiguities and share best practices, which helps ensure high-quality and reliable annotations.

What is the difference between Full Time Linguistic Annotation vs Data Labeling Specialist?

AspectFull Time Linguistic AnnotationData Labeling Specialist
CredentialsTypically requires linguistics or language-related degreesOften requires general technical or data-related training
Work EnvironmentOffice or remote, focused on language dataOffice or remote, focused on various data types
Industry UsageUsed in AI, NLP, speech recognition projectsUsed across AI, computer vision, and data science projects
Search & Comparison IntentOften searched by linguists or language specialistsOften searched by data science or AI professionals

Full Time Linguistic Annotation involves detailed language data work, often requiring linguistic expertise, while Data Labeling Specialists focus on broader data types with more general training. Both roles support AI development but differ in specialization and credentials.

More about Full Time Linguistic Annotation jobs
What cities are hiring for Full Time Linguistic Annotation jobs? Cities with the most Full Time Linguistic Annotation job openings:
What are the most commonly searched types of Linguistic Annotation jobs? The most popular types of Linguistic Annotation jobs are:
What states have the most Full Time Linguistic Annotation jobs? States with the most job openings for Full Time Linguistic Annotation jobs include:
What job categories do people searching Full Time Linguistic Annotation jobs look for? The top searched job categories for Full Time Linguistic Annotation jobs are:
Full Time Linguistic Annotation jobs near you
Infographic showing various Full Time Linguistic Annotation job openings in the United States as of June 2026, with employment types broken down into 100% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $58,415 per year, or $28.1 per hour.
AI Engineer

AI Engineer

Propio Language Services

Leawood, KS • On-site

$111K - $133K/yr

Full-time

Posted 17 days ago

Job description

Job Type
Full-time
Description
Propio Language Services is a provider of the highest quality interpretation, translation, and localization services. Our people take pride in every resource we offer, and our users always have access to cutting-edge technology, exceptional support, and collaborative user experiences. We are driven by our passion for innovation, growth, and bridging communication gaps in a diverse world. If you're passionate about delivering technology-driven solutions and building lasting client relationships while contributing to client growth, Propio could be the ideal place for you.
We are building AI-powered systems that enhance multilingual communication, improve interpreter workflows, and support next-generation AI applications across text, speech, and multimodal experiences.
Propio is hiring an AI Data Strategy Engineer / Applied Scientist, LLM Data to own the data strategy, curation pipelines, annotation workflows, and evaluation datasets that power our multilingual AI systems.
This is a hands-on technical role for someone who understands how to manage the full AI data lifecycle, from acquisition, curation, annotation, and quality control to evaluation datasets and post-training data, to directly improve model performance.
The ideal candidate can build scalable data pipelines, design high-quality annotation and QA processes, identify model failure modes, and close performance gaps through targeted data acquisition, curation, and synthetic data generation.
Requirements
  • Define the end-to-end data roadmap for multilingual and multimodal AI systems, including text, speech, translation, interpretation, low-resource languages, and agentic AI workflows.
  • Design and build dataset curation pipelines for training, post-training, and evaluation, including cleaning, deduplication, filtering, PII redaction, quality scoring, sampling, balancing, and versioning.
  • Create annotation schemas, labeling guidelines, QA rubrics, golden datasets, and reviewer workflows for multilingual, speech, translation, and agentic AI data.
  • Build evaluation datasets and benchmarks, analyze model failure modes, and translate performance gaps into targeted data improvements.
  • Support post-training data workflows such as SFT, instruction tuning, preference data, RLHF/DPO-style data, reward model data, and synthetic data generation.
  • Use modern annotation tools and AWS-based data infrastructure to scale secure, traceable, and compliant AI data workflows.

Qualifications
  • Bachelor's degree in Computer Science, Machine Learning, Data Science, Computational Linguistics, Linguistics, Statistics, or a related field, or equivalent practical experience.
  • 4+ years of experience in AI data, ML data operations, NLP data engineering, applied ML, speech/translation data, or LLM data workflows.
  • Strong hands-on experience with Python, SQL, and dataset curation pipelines.
  • Experience with annotation workflows, QA rubrics, evaluation datasets, or human-in-the-loop data processes.
  • Familiarity with multilingual NLP, speech data, translation data, low-resource languages, conversational AI, or agentic AI datasets.
  • Working knowledge of AWS data and ML tools such as S3, Glue, SageMaker, Bedrock, Lambda, Step Functions, EKS/ECS, IAM, or KMS.
  • Strong communication skills and ability to work with ML engineers, applied scientists, product teams, linguists, data teams, and vendors.

Preferred Qualifications
  • Master's or PhD in Computer Science, Machine Learning, NLP, Computational Linguistics, Data Science, Statistics, or a related field.
  • Experience with LLM post-training workflows such as SFT, instruction tuning, preference data, RLHF, DPO, reward modeling, or evaluation data generation.
  • Experience with synthetic data generation, active learning, weak supervision, LLM-as-judge workflows, or automated data quality scoring.
  • Experience with modern annotation and data platforms such as Labelbox, Scale AI, Prodigy, Argilla, Snorkel, Humanloop, or custom internal tooling.

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