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Annotation Labelling Jobs in Washington, DC (NOW HIRING)

B&A

Geospatial Data Analyst

Mclean, VA · On-site

$75K - $82K/yr

Create, edit, and position geographic names, labels, annotation, symbols, and other map elements using established placement techniques to minimize conflicts and ensure legibility. Develop and ...

Unissant

Data Scientist

Ashburn, VA · On-site

$99K - $164K/yr

Experience performing data mining, data analysis, image-data preparation, labeling or annotation, and training-set construction. * Familiarity with model-performance evaluation methods, such as ...

Unissant

Data Scientist

Ashburn, VA · On-site

$99K - $164K/yr

Experience performing data mining, data analysis, image-data preparation, labeling or annotation, and training-set construction.Familiarity with model-performance evaluation methods, such as ...

UNISSANT

Data Scientist

Ashburn, VA · On-site

Experience performing data mining, data analysis, image-data preparation, labeling or annotation, and training-set construction. * Familiarity with model-performance evaluation methods, such as ...

Bart & Associates

Geospatial Data Analyst

Mclean, VA · On-site

$75K - $82K/yr

... labels, annotation, symbols, and other map elements using established placement techniques to minimize conflicts and ensure legibility. · Develop and maintain map layouts and required product ...

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Annotation Labelling information

What is annotation labelling?

Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.

What are the key skills and qualifications needed to thrive as an annotation labelling specialist?

To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.

What are some common challenges faced by annotation labelling professionals, and how can they be managed?

Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.

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

AspectAnnotation LabellingData Labeling Specialist
CredentialsBasic technical skills, attention to detailSimilar skills, sometimes additional domain knowledge
Work EnvironmentData annotation platforms, remote or officeData annotation tasks, often remote or in-office
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, healthcare, retail
Search & ComparisonCommonly compared for entry-level data tasksRelated but broader role

Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.

What are popular job titles related to Annotation Labelling jobs in Washington, DC?

For Annotation Labelling jobs in Washington, DC, the most frequently searched job titles are:

What job categories do people searching Annotation Labelling jobs in Washington, DC look for?

The top searched job categories for Annotation Labelling jobs in Washington, DC are:

Annotation Labelling jobs near you

QA / Evaluation Lead

Washington, DC • On-site

Innodata Inc.
IT Services • 501 - 1,000 employees
Learn more about Innodata Inc.external

$45 - $50/hr

Full-time

Re-posted 4 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

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 QA/Evaluation Lead, you'll own quality and evaluation across the platform. You'll design the evaluation framework that measures whether our data services and outputs meet the bar, build repeatable test and validation processes, and give the team an objective read on readiness at each milestone. Partnering with the Delivery Owner and engineering leads, you'll turn quality from an afterthought into a measurable, demonstrable strength. It's a role for someone who thinks rigorously about evaluation and takes pride in evidence-backed quality.

Key Responsibilities:

  • Design and own the inter-annotator agreement (IAA) methodology for the Phase 1 demonstration corpus - metric selection (Cohen's kappa, Fleiss, Krippendorff's alpha), sampling design, adjudication workflow, and agreement thresholds
  • Define evaluation framework architecture: test and evaluation plans, IAA targets, drift detection gates, and model performance metrics per SOW Section 2.9
  • Configure and operate sampling-based quality control across the self-service and white-glove annotation paths during Phase D corpus production
  • Design and implement confidence-threshold escalation routing from automated annotation to senior-annotator adjudication
  • Validate quality scoring and IAA computation within the Innodata data layer
  • Support AI Solutions Engineer on evaluation design for SAM 2 and Frontier model API validation - define what 'good enough' looks like quantitatively
  • Produce evaluation framework documentation for the Phase 1 NPP closeout package, including per-DataCard documentation with the SA

Must-Have Qualifications:

  • Bachelor's degree in Statistics, Data Science, Computer Science, or related quantitative field required; Master's degree preferred. Equivalent experience may substitute for degree on a 2-for-1 basis.
  • 6+ years total professional experience, 4+ years in data quality, evaluation methodology, or QA on AI/ML programs
  • IAA methodology expertise - Cohen's kappa, Fleiss' kappa, Krippendorff's alpha: hands-on, not theoretical
  • Evaluation framework design for AI/ML training data programs
  • QC process design: sampling methodology, escalation workflows, adjudication protocols
  • Python for QC tooling, metric computation, and statistical analysis
  • Active Secret clearance with TS/SCI eligibility

Nice-to-Have Qualifications:

  • Prior DoD or IC data quality program experience
  • CVAT or equivalent annotation platform QC workflow configuration
  • Drift detection and model monitoring methodology
  • Experience with FMV / video annotation quality standards

The expected hourly salary range for this position is $45 to $50 p/hour, based on experience, skills, and qualifications.

Note to Candidates: 

This role is not a project manager with QC responsibilities - it is a methodology expert who owns the intellectual framework behind data quality on a federal AI program. The right candidate can walk into a meeting with Government evaluators and explain exactly why the evaluation design produces trustworthy labels. That conversation is part of Phase 2 positioning.

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