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Hourly Ai Data Annotation Jobs in Silver Spring, MD

Welo Data

Data Labeling Associate

Washington, DC

$34/hr

What if your language expertise could help improve the speech and voice AI systems used by millions of people worldwide? WHAT YOU'LL DO * Execute Data labelling and annotation tasks across speech and ...

Aston Carter

Paralegal

Fairfax, VA · On-site

$125/hr

Provide structured written feedback on complex legal content to support AI training, data annotation, and quality assurance processes. * Evaluate complex legal materials and apply strong judgment and ...

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All JobsHourly Ai Data Annotation JobsHourly Ai Data Annotation Jobs in Silver Spring, MD

Hourly Ai Data Annotation information

What is an hourly AI data annotation?

An Hourly AI Data Annotation job involves labeling, tagging, or categorizing data—such as images, text, or audio—to help train machine learning models. Annotators follow specific guidelines to ensure that the data is accurately labeled so that AI systems can learn to recognize patterns and make decisions. These jobs are typically paid by the hour and may require attention to detail, consistency, and sometimes familiarity with specialized annotation tools. This work is essential for improving the accuracy and usefulness of artificial intelligence applications.

What are the key skills and qualifications needed to thrive as an hourly AI data annotator?

To thrive as an AI Data Annotator, you need strong attention to detail, accuracy, and a basic understanding of data labeling concepts, typically supported by a high school diploma or equivalent. Familiarity with annotation tools such as Labelbox or Supervisely, and basic computer proficiency, are often required. Critical thinking, consistency, and effective communication are valuable soft skills in this role. These skills ensure high-quality, reliable data that directly improves the performance of AI and machine learning models.

What are some common challenges faced by hourly AI data annotators, and how can they be managed?

Hourly AI data annotators often encounter challenges such as repetitive tasks, maintaining high accuracy under time constraints, and adapting to evolving project guidelines. To manage these, it's important to take regular breaks to avoid fatigue, stay up to date with training materials, and communicate proactively with team leads if instructions are unclear. Many teams use collaborative tools and regular feedback sessions to support annotators and ensure consistent quality, making teamwork and attention to detail vital for success in this role.

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

AspectHourly Ai Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or in-office, flexible hoursRemote or in-office, flexible hours
Industry UsageAI, machine learning, tech companiesAI, machine learning, tech companies
Job FocusAnnotating data for AI training, often with specific instructionsLabeling data to help AI models learn, often similar tasks

Hourly Ai Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI systems. The main difference lies in terminology; 'Hourly Ai Data Annotation' emphasizes the paid hourly aspect and the specific task of annotating data for AI training, while 'Data Labeler' is a broader term used interchangeably in the industry. Both roles require similar skills and are used in the same industry sectors.

What are the most commonly searched types of Ai Data Annotation jobs in Silver Spring, MD?

The most popular types of Ai Data Annotation jobs in Silver Spring, MD are:

What job categories do people searching Hourly Ai Data Annotation jobs in Silver Spring, MD look for?

The top searched job categories for Hourly Ai Data Annotation jobs in Silver Spring, MD are:

What cities near Silver Spring, MD are hiring for Hourly Ai Data Annotation jobs?

Cities near Silver Spring, MD with the most Hourly Ai Data Annotation job openings:

Hourly Ai Data Annotation jobs near you
Infographic showing various Hourly Ai Data Annotation job openings in Silver Spring, MD as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 67% In-person, and 33% Remote job distribution.

AI, Data Platforms & Responsible AI Governance Lead - R-07B

Axyde Analytics

Washington, DC • On-site

$185 - $369/hr

Other

Posted 7 days ago

Job description

AI, Data Platforms & Responsible AI Governance Lead - R-07B
  • Washington, DC

AI, Data Platforms & Responsible AI Governance Lead - R-07B

Requisition: R-07B

Opportunity: 26-0015 / 1131PL26R0058

Location Anchor: Washington, DC 20001

Work mode: Fully remote / U.S.-based

Travel: Europe/Eurasia as assigned

Engagement: Contingent expert

Role purpose

Lead feasibility and project-scoping analysis for public-sector AI, data platforms, analytics, and responsible-AI governance within Europe/Eurasia digital-infrastructure opportunities. Translate policy goals and incomplete data environments into implementable use cases, architectures, governance controls, procurement requirements, budgets, schedules, and measurable outcomes.

Representative assignment problems
  • Assess data availability, quality, ownership, interoperability, privacy, residency, security, institutional capacity, and public acceptance.
  • Define AI/data-platform alternatives across cloud, data-center, edge, and connectivity environments.
  • Evaluate model purpose, training and inference data, human oversight, explainability, bias, safety, cybersecurity, vendor lock-in, lifecycle cost, and operating capacity.
  • Develop phased roadmaps, pilots, TOR/SOW requirements, budgets, schedules, acceptance tests, and responsible-AI controls.
  • Support digital-government, critical-infrastructure, service-delivery, urban/municipal, geospatial, or cross-border data use cases when assigned.
Responsibilities
  • Own AI/data analysis modules and integrate them with cybersecurity, data-center, connectivity, finance, regulatory, and regional work.
  • Produce evidence-traceable inputs for Preliminary Assessments, Project Reports, TORs, independent budgets, schedules, and the cumulative Final Report.
  • Distinguish proven use cases from speculative vendor claims and document limitations, dependencies, and recovery paths.
  • Define data/model governance, monitoring, incident, human-approval, and independent-review requirements.
  • Participate in remote analysis, stakeholder discovery, quality review, and international travel when assigned.
  • Protect sensitive information and disclose actual, potential, or apparent conflicts.
Minimum evidence-based qualifications
  • 10+ years of relevant data-platform, AI/ML, analytics, digital-government, or technology-governance experience, with dated named projects; the résumé must separately show the duration of direct AI work.
  • Direct responsibility for AI/data feasibility, architecture, governance, deployment roadmap, procurement/TOR/SOW, or implementation decisions—not only software development or tool use.
  • Evidence addressing data quality, privacy, interoperability, model risk, cybersecurity, human oversight, adoption, and measurable outcomes.
  • Public-sector, regulated-infrastructure, international-development, or Europe/Eurasia experience strongly preferred.
  • U.S. citizen or lawful permanent resident; Axyde will request written confirmation but not identity documents during recruiting.
  • Strong written English, independent technical judgment, contingent availability, and international-travel ability.
Evidence to submit
  • Current résumé with month/year dates and exact project responsibilities.
  • Short project list naming client, country, period, data/AI use case, architecture or governance product, and outcome.
  • One nonproprietary example of an AI/data options analysis, roadmap, governance framework, procurement package, or TOR.
  • Current city/state, hourly-rate expectation, availability, travel status, and employment/country/customer/conflict restrictions.
Application questions
  1. 1. Identify three relevant data/AI projects and state the years of direct AI work separately from general IT tenure.
  2. 2. Describe one public-sector or regulated-infrastructure use case and the decision product you led.
  3. 3. Explain how you addressed data quality, privacy/residency, cybersecurity, bias/model risk, human oversight, and vendor lock-in.
  4. 4. Describe one roadmap, procurement package, TOR/SOW, pilot design, or acceptance framework you produced.
  5. 5. Identify Europe/Eurasia countries/languages and international-development experience.
  6. 6. Are you a U.S. citizen or lawful permanent resident? Answer only yes/no/category; do not upload identity documents.
  7. 7. Confirm availability, travel ability, conflicts/restrictions, employer approvals, and rate expectation.
Contingent-opportunity notice

This posting supports a competitive federal proposal. It is not an offer or guarantee of employment, subcontract, hours, travel, award, or assignment. Selection is conditioned on written résumé authorization, evidence review, eligibility, availability, conflict review, rate agreement, proposal strategy, award, and customer direction. Do not submit passports, Social Security numbers, immigration documents, classified/export-controlled information, or customer-proprietary material.

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