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Home Based Linguistic Annotation Jobs (NOW HIRING)

Propio Language Services

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Use modern annotation tools and AWS-based data infrastructure to scale secure, traceable, and ... Bachelor's degree in Computer Science, Machine Learning, Data Science, Computational Linguistics ...

Propio

AI Engineer

Leawood, KS · On-site

$111K - $133K/yr

Use modern annotation tools and AWS-based data infrastructure to scale secure, traceable, and ... Bachelor's degree in Computer Science, Machine Learning, Data Science, Computational Linguistics ...

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Home Based Linguistic Annotation information

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$45K

$58.4K

$97.5K

How much do home based linguistic annotation jobs pay per year?

As of Jul 4, 2026, the average yearly pay for home based 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 is home based linguistic annotation?

Home based linguistic annotation involves working remotely to label or categorize language data, such as text or audio, for use in natural language processing (NLP) and artificial intelligence projects. Annotators may identify parts of speech, tag named entities, transcribe speech, or mark sentiment in various types of linguistic data. This work is essential for training machine learning models to better understand human language and improve technologies like voice assistants, translation apps, and chatbots. Typically, it requires strong language skills, attention to detail, and familiarity with linguistic concepts.

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

To excel as a Home Based Linguistic Annotation Specialist, you need a solid grasp of linguistics, attention to detail, and proficiency in relevant languages, usually supported by a degree in linguistics or related fields. Familiarity with annotation platforms, data labeling tools, and sometimes scripting languages like Python is often required. Strong time management, self-motivation, and effective communication are important soft skills for remote collaboration and meeting project deadlines. These skills ensure high-quality, accurate data annotation, which is crucial for training reliable AI and language processing systems.

What are some common challenges faced by home-based linguistic annotators, and how can they be addressed?

Home-based linguistic annotators often encounter challenges such as maintaining focus during repetitive tasks, managing deadlines across multiple projects, and ensuring consistency in annotation quality. To address these, it helps to establish a dedicated, distraction-free workspace and adhere to a structured daily routine. Regular communication with project managers and fellow annotators through online platforms also ensures clarity on guidelines and fosters a sense of teamwork, even while working remotely.

What is the difference between Home Based Linguistic Annotation vs Transcription Specialist?

AspectHome Based Linguistic AnnotationTranscription Specialist
Required CredentialsBasic language skills, attention to detailTyping speed, language proficiency, sometimes certification
Work EnvironmentRemote, flexible hoursRemote or on-site, flexible or fixed hours
Industry UsageAI training, linguistic researchMedia, legal, medical transcription
Common Search IntentJobs involving language annotation tasksJobs involving audio/video transcription

Home Based Linguistic Annotation involves labeling and tagging language data for AI models, requiring linguistic skills and attention to detail. Transcription Specialists focus on converting audio or video recordings into written text, often needing fast typing and language proficiency. While both roles are remote and involve language skills, they serve different industry needs and require distinct skill sets.

More about Home Based Linguistic Annotation jobs
What cities are hiring for Home Based Linguistic Annotation jobs? Cities with the most Home Based 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 Home Based Linguistic Annotation jobs? States with the most job openings for Home Based Linguistic Annotation jobs include:
What job categories do people searching Home Based Linguistic Annotation jobs look for? The top searched job categories for Home Based Linguistic Annotation jobs are:
Home Based Linguistic Annotation jobs near you
Infographic showing various Home Based Linguistic Annotation job openings in the United States as of June 2026, with employment types broken down into 90% Full Time, 1% Temporary, and 9% Contract. Highlights an 77% Physical, 1% Hybrid, and 22% 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 5 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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