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Linguistic Data Annotation Jobs in Texas (NOW HIRING)

Arrive Logistics

Data Scientist II

Austin, TX · On-site

... annotation guidelines and ensuring label quality. • Evaluate and apply the appropriate approach ... linguistics, or related) and 2-4 years of applied ML or data science experience, or equivalent ...

Arrive Logistics

Data Scientist II

Austin, TX · On-site

... annotation guidelines and ensuring label quality. * Evaluate and apply the appropriate approach for ... Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics ...

Arrive Logistics

Data Scientist II

Austin, TX · On-site +1

... annotation guidelines and ensuring label quality. * Evaluate and apply the appropriate approach for ... Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics ...

Sourceability

Principal NLP Scientist

Austin, TX

Data preparation, annotation strategy, and labeling quality * Model evaluation, monitoring, and ... PhD in Computer Science, Machine Learning, Artificial Intelligence, Computational Linguistics ...

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Linguistic Data Annotation information

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

Linguistic data annotation often involves interpreting ambiguous or context-dependent language, which can be challenging, especially when dealing with idiomatic expressions, slang, or multiple languages. Consistency in labeling is critical, so annotators must regularly review guidelines and communicate with team members to resolve uncertainties. Many teams use collaborative tools and periodic calibration sessions to ensure high-quality, uniform annotations. Staying detail-oriented and open to feedback helps annotators continuously improve their work and adapt to evolving project requirements.

What is linguistic data annotation?

Linguistic data annotation is the process of labeling or tagging language data, such as text or speech, with relevant linguistic information. This can include marking parts of speech, named entities, syntactic structures, or semantic roles to help train and evaluate natural language processing (NLP) models. Annotators follow specific guidelines to ensure consistency and accuracy, making the data usable for machine learning tasks. Linguistic data annotation is essential for developing AI applications like chatbots, translation systems, and speech recognition software.

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

AspectLinguistic Data AnnotationData Labeling Specialist
CredentialsBasic understanding of linguistics, language skillsGeneral data labeling skills, attention to detail
Work EnvironmentTech companies, AI development teamsData annotation firms, AI/ML companies
Industry UsageNatural language processing, speech recognitionComputer vision, image and video annotation
Search/Comparison IntentUnderstanding linguistic annotation rolesGeneral data labeling roles

While both roles involve preparing data for AI models, Linguistic Data Annotation focuses on language-specific tasks like transcribing, tagging, and annotating text or speech data. Data Labeling Specialists handle a broader range of data types, including images and videos, with less emphasis on linguistic expertise. The roles often overlap in AI development but differ in the type of data and skills required.

What are the key skills and qualifications needed to thrive as a linguistic data annotator?

To thrive as a Linguistic Data Annotator, you need a strong grasp of linguistics, attention to detail, and proficiency in at least one language, often supported by a degree in linguistics or a related field. Familiarity with annotation tools, text analysis software, and basic data management systems is typically required. Exceptional analytical thinking, consistency, and the ability to follow detailed guidelines are crucial soft skills in this position. These skills ensure high-quality, accurate data that directly supports the development and training of language technologies.
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Linguistic Data Annotation jobs near you
Infographic showing various Linguistic Data Annotation job openings in Texas as of August 2026, with employment types broken down into 61% Full Time, 5% Part Time, 8% Temporary, and 26% Contract. Highlights an 74% In-person, and 26% Remote job distribution.

Data Scientist II

Arrive Logistics

Austin, TX • On-site

Full-time

Re-posted 19 days ago

Arrive Logistics rating

5.5

Company rating: 5.5 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

Job description

Job Summary:
Arrive Logistics is a leading transportation and technology company in North America, committed to providing employees with a meaningful work experience. The Data Scientist II will work closely with Data Science, Product, and Engineering teams to build and improve ML and AI systems that drive operational value, focusing on text and language-based applications.
Responsibilities:
• Develop, evaluate, and iterate on NLP and LLM-based systems, including text classification, information extraction, and context retrieval pipelines.
• Build measurement and evaluation frameworks — both offline and online — to assess where and why systems are underperforming and quantify the impact of improvements.
• Develop golden test datasets and define methodologies for creating and maintaining them over time, including designing annotation guidelines and ensuring label quality.
• Evaluate and apply the appropriate approach for language tasks — whether prompt engineering, fine-tuning, or classical NLP methods — including modern retrieval and RAG architectures and LLM evaluation methodologies, based on the problem and available data.
• Perform structured analysis of system performance to surface failure modes, data gaps, and high-value areas for investment, applying sound statistical reasoning to evaluation results.
• Partner with engineers to support deployment, integration, and monitoring of ML and AI systems in production.
• Contribute to standards and best practices around deploying, evaluating, and monitoring text and language-based ML systems.
• Document work clearly and maintain knowledge artifacts that make systems understandable and maintainable over time.
• Collaborate with senior data scientists and cross-functional partners to translate business needs into well-scoped technical solutions, including communicating findings and recommendations to non-technical stakeholders.
Qualifications:
Required:
• Bachelor's or Master's degree in a quantitative field (computer science, statistics, linguistics, or related) and 2–4 years of applied ML or data science experience, or equivalent practical experience.
• Hands-on experience building or improving NLP or LLM-based systems in applied settings.
• Familiarity with text classification, information extraction, or other NLP tasks — and an understanding of where these systems fail.
• Experience with both prompt engineering and fine-tuning approaches for language tasks, with the judgment to know when to apply each.
• Familiarity with modern retrieval strategies and RAG architectures and how they affect LLM system performance.
• Experience with Hugging Face Transformers for text classification or related NLP tasks.
• Experience contributing to evaluation frameworks, test sets, or performance diagnostics for ML systems, including comfort with statistical methods for measuring model performance.
• Proficiency in Python and SQL, and comfort working with structured and unstructured data.
• Ability to operate effectively in ambiguous problem spaces — scoping technical approaches when requirements are not fully defined.
• Strong written communication skills; able to document systems and findings clearly and present recommendations to non-technical stakeholders.
Preferred:
• Experience designing data annotation workflows, labeling guidelines, or label quality processes is a plus.
• Experience with model deployment, monitoring, or production ML workflows is a plus.
• Familiarity with LangChain and LangSmith or similar LLM orchestration and observability tooling is a plus.
• Transportation or logistics industry experience is a plus.
Company:
Arrive Logistics is a carrier and customer-centric logistics company that focuses on new standards for service in freight. Founded in 2014, the company is headquartered in Austin, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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