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Data Annotation Jobs in Gaithersburg, MD (NOW HIRING)

CaseGuard

Data Annotation Specialist

Arlington, VA · On-site

$40K - $50K/yr

Perform accurate data entry and data validation to ensure high-quality datasets. * Review, label, and categorize images to support machine learning and AI model training. * Follow detailed annotation ...

CaseGuard

Data Annotation Specialist

Arlington, VA

$40K - $50K/yr

Perform accurate data entry and data validation to ensure high-quality datasets. * Review, label, and categorize images to support machine learning and AI model training. * Follow detailed annotation ...

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

You'll integrate synthetic data generation into the pipeline, stand up and tune the annotation toolchain, and orchestrate reproducible ML workflows that the rest of the team can build on. You'll ...

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Data Annotation information

What does a typical workday look like for someone in a Data Annotation role?

A typical workday as a Data Annotator involves reviewing datasets—such as images, audio, text, or video—and accurately labeling or categorizing information according to specific project guidelines. Most Data Annotators work independently, but they often collaborate with project managers or data scientists to clarify requirements and resolve ambiguities. Tasks may be repetitive, but adhering to precise standards is vital for maintaining data quality. Work environments can range from technology companies to remote or freelance settings, and advancement opportunities exist as team leads or quality assurance specialists for those who excel in consistency and reliability.

Is data annotation a genuine job?

Data annotation is a legitimate job that involves labeling data such as images, text, or audio to help train machine learning models. It often requires attention to detail and familiarity with annotation tools, and can be found in various industries like technology and healthcare.

Does data annotation pay well?

Data annotation jobs typically offer entry-level pay that varies depending on the employer, location, and complexity of the tasks. While some positions pay hourly wages comparable to other administrative or clerical roles, experienced annotators working on specialized projects or with advanced tools can earn higher rates. Overall, data annotation is often considered an entry-level position with moderate pay potential.

What is a Data Annotation job?

A Data Annotation job involves labeling and categorizing data, such as text, images, audio, or video, to help train machine learning models. Annotators apply tags, bounding boxes, or classifications to data based on specific guidelines. This process improves the accuracy of AI systems in recognizing patterns and making predictions. Many data annotation jobs require attention to detail and familiarity with specific domains. It is commonly used in applications like autonomous driving, natural language processing, and computer vision.

How hard is it to get hired by data annotation?

Getting hired for a data annotation role generally requires basic computer skills, attention to detail, and sometimes familiarity with specific tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible, though competition can vary based on the employer and location.

What are the key skills and qualifications needed to thrive in the Data Annotation position, and why are they important?

To thrive in Data Annotation, you need strong attention to detail, accuracy, and basic data handling skills, often supported by a high school diploma or equivalent. Familiarity with annotation platforms, data labeling software, or content management systems is frequently required, though specific certifications are rare. Excellent communication, time management, and the ability to focus on repetitive tasks distinguish top performers in this role. These skills are crucial because accurate and consistent data annotation directly impacts the quality of machine learning models and AI applications.

What does a data annotator do?

A data annotator labels and tags data such as images, text, or videos to help machine learning models understand and learn from the data. They use tools and follow guidelines to ensure accuracy and consistency, often working with large datasets in a structured environment. Attention to detail and knowledge of annotation tools are important for this role.
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Data Annotation jobs near you
Infographic showing various Data Annotation job openings in Gaithersburg, MD as of July 2026, with employment types broken down into 2% Locum Tenens, 35% Full Time, 25% Part Time, 2% Contract, 35% Nights, and 1% Summer. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.

Data & Annotation Engineer

Innodata Inc.

Washington, DC

$55 - $60/hr

Other

Posted 20 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

161st of 241 rated software companies

Job description

About the Program: 

Innodata's Federal Practice builds the trusted data layer for critical infrastructure Trust & Safety work. Partnering with a leading systems integrator, we're delivering a modern, governed data services platform in a secure federal (IL4) environment. Over an intensive 20-week phase, you'll help stand up a data services storefront, a DataCard governance framework, synthetic data integration, and Databricks write-back capabilities.

About the Role: 

As the Data/Annotation Engineer, you'll be hands-on with the data itself. You'll administer the annotation toolchain, manage annotation workflows across the corpus, and produce the per-dataset documentation that feeds our governance framework. You'll work with the AI Solutions Engineer to ensure the data going into our models is accurate, well-labeled, and fully traceable. This role is for someone detail-obsessed who understands that great AI starts with disciplined, well-governed data.

Key Responsibilities:

  • Receive, validate, ingest, and ontology-map the ODIN mission-aligned corpus from AFS delivery
  • Produce the ODIN load report: corpus description, ontology mapping, readiness state
  • Configure CVAT annotation pipeline against the Phase 1 starter kit rule pack
  • Operate both self-service and lightweight white-glove annotation paths during Phase D corpus production
  • Produce 50-100 label demonstration corpus across synthetic and mission-aligned content
  • Support QA/Evaluation Lead on QC execution and corpus annotation dry-runs
  • Associate DataCard provenance records with annotated and synthetic outputs in coordination with the Solution Architect

Must-Have Qualifications:

  • Bachelor's degree in Data Science, Computer Science, or related field preferred. Equivalent experience may substitute for degree on a 2-for-1 basis.
  • 5+ years total professional experience, 3+ years in data engineering or annotation operations
  • CVAT - deployment and day-to-day operation required; this is not a nice-to-have
  • Annotated dataset ingest pipelines: schema mapping, format validation, ontology alignment
  • Full-motion video (FMV) annotation concepts and tooling
  • Python scripting for data wrangling, validation, and format conversion
  • Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

  • Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis
  • CVAT annotation platform - AI feature configuration and operation
  • DoD or IC data program experience: CUI, distribution statements, federal data governance
  • Evaluation design for AI/ML training data: IAA methodology, drift detection, model performance measurement
  • Video understanding or FMV annotation experience
  • DataCard or ML data provenance framework familiarity

The expected hourly salary range for this position is $55 to $60 p/hour, based on experience, skills, and qualifications.

Note to Candidates: 

Phase D corpus production (Weeks 17-19) is the core demonstration deliverable. Candidates must be genuinely comfortable operating CVAT at production quality against a mission dataset under a milestone deadline

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