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Linguistic Data Annotation Jobs in Massachusetts

Hatch IT

NLP/Linguistics Software Engineer

Somerville, MA · On-site

$125K - $150K/yr

Experience with data quality evaluation, data annotation, or guideline design, preferably for linguistics. * Familiarity with Elasticsearch internals or other search/retrieval-based systems.

Babel Street

NLP/Linguistics Software Engineer

Somerville, MA · On-site

$125K - $150K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Since our platform processes data from around the globe, your linguistic insights can directly ... Experience with data quality evaluation, data annotation, or guideline design, preferably for ...

Hatch IT

NLP/Linguistics Software Engineer

Somerville, MA · On-site

$125K - $150K/yr

Experience with data quality evaluation, data annotation, or guideline design, preferably for linguistics. * Familiarity with Elasticsearch internals or other search/retrieval-based systems.

Hatch IT

NLP/Linguistics Software Engineer

Somerville, MA · On-site

$125K - $150K/yr

Experience with data quality evaluation, data annotation, or guideline design, preferably for linguistics. * Familiarity with Elasticsearch internals or other search/retrieval-based systems.

Babel Street

NLP/Linguistics Software Engineer

Somerville, MA · On-site

$125K - $150K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Since our platform processes data from around the globe, your linguistic insights can directly ... Experience with data quality evaluation, data annotation, or guideline design, preferably for ...

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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.
What are popular job titles related to Linguistic Data Annotation jobs in Massachusetts? For Linguistic Data Annotation jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Linguistic Data Annotation jobs in Massachusetts look for? The top searched job categories for Linguistic Data Annotation jobs in Massachusetts are:
What cities in Massachusetts are hiring for Linguistic Data Annotation jobs? Cities in Massachusetts with the most Linguistic Data Annotation job openings:
Linguistic Data Annotation jobs near you
Infographic showing various Linguistic Data Annotation job openings in Massachusetts 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.

NLP/Linguistics Software Engineer

Hatch IT

Somerville, MA • On-site

$125K - $150K/yr

Full-time

Re-posted 20 days ago

Job description

hatch I.T. is partnering with Babel Street to find an NLP/Linguistics Software Engineer. Please see details below:

About the Role

Babel Street is looking for a Software Engineer to join their Analytics Group. This is an execution-focused "builder" role for an engineer early in their career who wants to work at the intersection of NLP algorithms, search engines, and data science techniques. In this role, you will help create the next generation of architecture and components for their analytics platform, focusing specifically on their record matching functionality. You will bridge the gap between linguistic theory and practical AI applications, helping us implement practical, innovative text analytics and AI-driven features. You will work closely with senior engineers to learn how to deliver software that is safe, reliable, and production-ready.

About the Company

Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. They deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage.  The actionable insights we deliver safeguard lives and protect critical assets around the world.  Babel Street is headquartered in Reston, Virginia, with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K.

What you will do:
  • Implement and Maintain: Write high-quality, maintainable code to support the analytics platform and its record matching components.
  • Bridge Theory and Practice: Take theoretical ideas from linguistics and data science and implement them as practical software features.
  • Support Search Internals: Help optimize and maintain search engine components, including Elasticsearch data modeling and performance tuning.
  • Collaborate and Learn: Participate in agile sprint planning and work daily with senior partners to translate project requirements into technical solutions.
  • Build Scalable Systems: Assist in designing and shipping robust APIs and scalable architectures that integrate into our AI-native platform.
What you will bring:

Required:

  • 2–4 years of professional software engineering experience (including high-impact internships or projects).
  • Proficiency in Java (our core analytics language) or Python (for AI/ML integrations).
  • Problem Solver: Ability to work across teams and make steady progress in ambiguous problem spaces.
  • Educational Foundation: Bachelor's degree in Computer Science, Linguistics, or a related technical field.

Preferred (Nice to Have):

  • Foundation in Data Science: Experience with data quality evaluation, data annotation, or guideline design, preferably for linguistics.
  • Familiarity with Elasticsearch internals or other search/retrieval-based systems.
  • Exposure to computational linguistics or natural language processing (NLP).
  • Interest in Kubernetes and cloud-native architectures.
What success looks like:
  • Month 1–2: Ramp up on the analytics stack and record matching architecture; ship your first initial changes to production.
  • Month 3–4: Take ownership of a specific component or pipeline improvement with guidance, including full testing and documentation.
  • Month 5–6: Deliver a measurable improvement to record matching quality or pipeline reliability and contribute to team design discussions.
Why this role matters:
The record matching functionality is where Babel Street’s signals become usable intelligence. Do you care about provenance, explainability, and trust? When a match decision affects whether someone is onboarded or investigated, "the model said so" is not good enough. You will help build systems where every match is defensible, auditable, and tunable — a rare luxury in modern ML-heavy stacks. Do you speak multiple languages? Since our platform processes data from around the globe, your linguistic insights can directly inform how we build and polish the NLP and computational linguistics components that make our record matching world-class.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.