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Data Annotation For Ai Jobs in Washington, DC (NOW HIRING)

Jobtailor

Data Annotation Engineer

Washington, DC · On-site

$120 - $180/hr

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 ...

CaseGuard

Data Annotation Specialist

Arlington, VA · On-site

$40K - $50K/yr

This role is responsible for redacting sensitive information from files and reviewing, labeling ... Review, label, and categorize images to support machine learning and AI model training. * Follow ...

CaseGuard

Data Annotation Specialist

Arlington, VA

$40K - $50K/yr

This role is responsible for redacting sensitive information from files and reviewing, labeling ... Review, label, and categorize images to support machine learning and AI model training. * Follow ...

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

Implement Frontier model API integration for synthetic data fidelity validation: prompt engineering, response validation, quality scoring * Configure AI-assisted annotation features: confidence ...

YO AI Labs

AI Consulting Expert - Remote

Washington, DC · Remote

$100 - $200/hr

Evaluate and refine AI-generated responses for accuracy, clarity, and alignment with business ... Data Annotation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail ...

YO AI Labs

AI Finance Expert - Remote

Washington, DC · Remote

$100 - $200/hr

Analyze, review, and edit AI-generated financial content for accuracy, clarity, and relevance ... Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ...

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Data Annotation For Ai information

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.

What are the key skills and qualifications needed to thrive as a data annotation specialist for AI, and why are they important?

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What is the difference between Data Annotation For Ai vs Data Labeler?

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What are popular job titles related to Data Annotation For Ai jobs in Washington, DC?

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

What job categories do people searching Data Annotation For Ai jobs in Washington, DC look for?

The top searched job categories for Data Annotation For Ai jobs in Washington, DC are:

Data Annotation For Ai jobs near you

Data & Annotation Engineer

Innodata Inc.

Washington, DC

$55 - $60/hr

Full-time

Re-posted 12 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

164th of 245 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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