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Data Annotation Ai Trainer Jobs in Reston, VA (NOW HIRING)

CaseGuard

Data Annotation Specialist

Arlington, VA · On-site

$40K - $50K/yr

Review, label, and categorize images to support machine learning and AI model training. * Follow ... Handle sensitive data with discretion and comply with privacy and security policies. * Meet ...

CaseGuard

Data Annotation Specialist

Arlington, VA

$40K - $50K/yr

Review, label, and categorize images to support machine learning and AI model training. * Follow ... Handle sensitive data with discretion and comply with privacy and security policies. * Meet ...

YO AI Labs

AI Finance Expert - Remote

Washington, DC · Remote

$100 - $200/hr

AI Finance Domain Expert Job Type: Contractor (Part-Time) Location: Remote Job Overview We are ... Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ...

Innodata Inc.

AI Solutions Engineer

Washington, DC

$75 - $80/hr

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 ...

YO AI Labs

AI Consulting Expert - Remote

Washington, DC · Remote

$100 - $200/hr

Job Title: AI Consulting Domain Remote Job Type: Contractor (Part-Time) Location: Remote Job ... Data Annotation * Fact Checking * Independent Research * Problem-Solving * Attention to Detail ...

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All JobsData Annotation Ai Trainer JobsData Annotation Ai Trainer Jobs in Reston, VA

Data Annotation Ai Trainer information

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How much do data annotation ai trainer jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for data annotation ai trainer in Reston, VA is $25.74, according to ZipRecruiter salary data. Most workers in this role earn between $19.52 and $27.50 per hour, depending on experience, location, and employer.

What is a data annotation AI trainer?

A Data Annotation AI Trainer is responsible for labeling and annotating data to help train machine learning models. This involves identifying objects, tagging text, or categorizing images to improve AI accuracy. The role requires attention to detail and an understanding of guidelines to ensure high-quality labeled data. AI trainers work closely with data scientists and engineers to refine model performance through precise annotations.

What does a data annotation AI trainer do?

As a Data Annotation Ai Trainer, your typical day involves reviewing and labeling large datasets, providing feedback to annotation teams, and ensuring that data quality meets project standards. You'll often collaborate with data scientists, machine learning engineers, and project managers to clarify guidelines and resolve ambiguities. Periodically, you may help develop or refine documentation and training materials to improve annotation consistency. The role requires both independent work and open communication to maintain high accuracy and support AI development initiatives.

What are the key skills and qualifications needed to thrive as a data annotation AI trainer?

To thrive as a Data Annotation Ai Trainer, you need a keen attention to detail, basic data analysis skills, and familiarity with machine learning concepts, often supported by a relevant degree or coursework. Experience with annotation tools like Labelbox, Supervisely, or similar platforms, along with knowledge of data privacy standards, is commonly required. Strong communication, problem-solving ability, and patience help you work effectively in teams and ensure data quality. These skills are essential because they directly influence the accuracy and effectiveness of AI models trained using annotated data.

How much do data annotation AI trainers make?

Data annotation AI trainers typically earn between $15 and $30 per hour, depending on experience, location, and the complexity of the annotation tasks. Salaries can range from entry-level rates to higher wages for specialized skills or advanced tools proficiency.

Is data annotation AI trainer job legitimate?

Data annotation AI trainer jobs are legitimate roles involving labeling data to help train machine learning models. These positions often require attention to detail and familiarity with annotation tools, and they are commonly offered by tech companies and data labeling firms. However, job seekers should verify the employer's credibility to avoid scams.

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Data Annotation Ai Trainer jobs near you
Infographic showing various Data Annotation Ai Trainer job openings in Reston, VA as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $53,530 per year, or $25.7 per hour.

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

165th of 246 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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