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Manager Ai Annotation Jobs in Washington (NOW HIRING)

Configure NiFi FMV codec validation layer (H.264, H.265, MPEG-4) above AFS-managed substrate * Validate AI feature integration end-to-end across storefront, annotation platform, and DataCard write ...

Senior Imagery Analyst (Data Annotation)

Falls Church, VA · On-site

$91K - $115K/yr

... AI/ML development. Responsibilities : * Provide quality assurance reviews of annotated geospatial ... Collaborate with peers, quality lead, program managers, and other critical partners to align data ...

Review, label, and categorize images to support machine learning and AI model training. * Follow ... Meet productivity and accuracy benchmarks while managing multiple tasks. Qualifications: * Bachelor ...

Review, label, and categorize images to support machine learning and AI model training. * Follow ... Meet productivity and accuracy benchmarks while managing multiple tasks. Qualifications: * Bachelor ...

Support AI Solutions Engineer on evaluation design for SAM 2 and Frontier model API validation ... CVAT or equivalent annotation platform QC workflow configuration * Drift detection and model ...

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Manager Ai Annotation information

What is the difference between Manager Ai Annotation vs Data Annotator?

AspectManager Ai AnnotationData Annotator
Required CredentialsBachelor's degree in related field, experience in AI projectsHigh school diploma or equivalent, on-the-job training
Work EnvironmentTeam management, project oversight, collaboration with data scientistsData labeling tasks, working with annotation tools
Industry UsageAI development, machine learning projectsData preparation for AI models
Search & Comparison IntentUnderstanding managerial roles in AI annotationEntry-level annotation tasks

The main difference between Manager Ai Annotation and Data Annotator lies in their responsibilities and experience. Managers oversee annotation projects, coordinate teams, and ensure quality, requiring leadership skills and experience. Data Annotators focus on labeling data accurately under supervision. Managers typically have higher credentials and work in strategic roles, while annotators perform the hands-on labeling tasks essential for AI training.

What are the most commonly searched types of Ai Annotation jobs in Washington? The most popular types of Ai Annotation jobs in Washington are:
What are popular job titles related to Manager Ai Annotation jobs in Washington? For Manager Ai Annotation jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Manager Ai Annotation jobs in Washington look for? The top searched job categories for Manager Ai Annotation jobs in Washington are:
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Data & Annotation Engineer

Innodata Inc.

Washington, DC

$55 - $60/hr

Other

Posted 22 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


What Innodata employees say

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