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Day Data Annotation Jobs in Washington (NOW HIRING)

Innodata Inc.

Data & Annotation Engineer

Washington, DC

$55 - $60/hr

As the Data/Annotation Engineer, you'll be hands-on with the data itself. You'll administer the ... CVAT - deployment and day-to-day operation required; this is not a nice-to-have * Annotated dataset ...

Jobtailor

Technical Project Manager

Washington, DC · On-site

$120 - $150/hr

* Own day-to-day WBS execution and schedule maintenance against the 24-week program schedule * Draft ... Administer CVAT + SAM 2 toolchain in coordination with the Data & Annotation Engineer * Facilitate ...

New

Welo Data

Data Labeling Associate

Washington, DC

$34/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

WHAT YOU'LL DO * Execute Data labelling and annotation tasks across speech and voice datasets ... Paid Vacation (6 days) * Paid Company Holidays * Paid Sick Leave * Employee Assistance Program

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All JobsDay Data Annotation JobsDay Data Annotation Jobs in Washington

Day Data Annotation information

What is a day data annotation job?

Day Data Annotation jobs involve reviewing and tagging data, such as images, text, audio, or video, during regular daytime hours. Annotators help prepare datasets for machine learning and artificial intelligence by labeling or categorizing information according to specific guidelines. This work is essential for training algorithms to recognize patterns, objects, or language. Day Data Annotation can be done remotely or in-office, and it often requires attention to detail and good communication skills.

What are the key skills and qualifications needed to thrive as a day data annotation specialist?

To excel as a Day Data Annotation Specialist, you need strong attention to detail, data entry accuracy, and a solid understanding of the subject matter being annotated, often supported by a high school diploma or relevant experience. Familiarity with annotation tools, spreadsheets, and data management software is typically required. Excellent concentration, time management, and clear communication skills help professionals stand out in this role. These abilities are crucial to ensure high-quality, consistent data labeling that directly impacts the performance of machine learning models and downstream business applications.

What are some common challenges faced by day data annotation specialists and how can they be addressed?

Day Data Annotation specialists often encounter challenges such as maintaining high accuracy while handling repetitive tasks, interpreting ambiguous data, and meeting tight deadlines. To address these, it's important to develop strong attention to detail, use project guidelines as references, and communicate with team leads or peers when uncertainties arise. Many organizations also provide regular feedback and quality assurance checks, which help annotators improve their performance and consistency over time.

What is the difference between Day Data Annotation vs Data Labeler?

AspectDay Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, collaborative teamsRemote or on-site, independent work
Industry UsageAI/ML companies, tech firmsAI/ML, data processing companies
Job FocusAnnotating data for machine learning modelsLabeling data to train AI systems

Day Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI. Day Data Annotation often involves more detailed annotation tasks, while Data Labelers may perform broader labeling activities. Both roles require basic technical skills and are vital in AI development across tech industries.

Can I do data annotation with no experience?

Day data annotation jobs often do not require prior experience, as training is typically provided to teach you how to label data accurately. Basic computer skills and attention to detail are usually sufficient to start, and some roles may require familiarity with annotation tools or platforms. Entry-level positions are common and can serve as a stepping stone to more advanced data-related roles.

What are the most commonly searched types of Data Annotation jobs in Washington?

The most popular types of Data Annotation jobs in Washington are:

What cities in Washington are hiring for Day Data Annotation jobs?

Cities in Washington with the most Day Data Annotation job openings:

Day Data Annotation jobs near you
Infographic showing various Day Data Annotation job openings in Washington as of June 2026, with employment types broken down into 1% As Needed, 63% Full Time, 32% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Data & Annotation Engineer

Innodata Inc.

Washington, DC

$55 - $60/hr

Full-time

Re-posted 9 days ago

Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz

163rd 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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