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Home Based Data Annotation Jobs in Washington, DC

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

Video understanding or FMV annotation experience * DataCard or ML data provenance framework familiarity The expected hourly salary range for this position is $75 to $80 p/hour, based on experience ...

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Home Based Data Annotation information

Can I do data annotation with no experience?

Home Based Data Annotation jobs often do not require prior experience, as training is typically provided. Basic computer skills and attention to detail are usually sufficient to start, making it accessible for beginners. Familiarity with annotation tools can be helpful but is not always mandatory.

Is it hard to get hired for data annotation?

Home Based Data Annotation jobs typically have moderate competition, and hiring depends on the applicant's attention to detail, accuracy, and ability to follow guidelines. Many positions require basic computer skills and sometimes prior experience with annotation tools, but they often do not demand formal certifications. Candidates who demonstrate reliability and precision have good chances of being hired.

Is data annotation a legitimate 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 basic computer skills, and many companies offer remote, flexible positions. However, job seekers should be cautious of scams and verify the legitimacy of employers before applying.

What is the difference between Home Based Data Annotation vs Data Labeler?

AspectHome Based Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailSimilar; no formal certifications typically required
Work EnvironmentRemote, home-basedRemote or in-office, depending on employer
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job TasksAnnotating data for training AI modelsLabeling data for AI training

Both roles involve labeling data for AI systems, often working remotely. Home Based Data Annotation emphasizes working from home with flexible hours, while Data Labeler may work in various environments. Both positions are essential in AI development and share similar skills and industry usage.

How to make 2000 a week working from home?

Home Based Data Annotation jobs typically pay per task or project, and earning $2000 weekly requires completing a high volume of accurately labeled data, often involving skills in image, text, or audio annotation. To reach this income level, consistent work, efficiency, and experience are essential, and some roles may offer bonuses or higher rates for specialized tasks or faster turnaround times.
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Home Based Data Annotation jobs near you
Data & Annotation Engineer

Data & Annotation Engineer

Innodata Inc.

Washington, DC

$55 - $60/hr

Other

Posted 15 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

162nd 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

What Innodata employees say

Hours and flexibility

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