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

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Freelance Ai Data Annotation information

See Reston, VA salary details

$12

$21

$36

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

As of Sep 14, 2026, the average hourly pay for freelance ai data annotation in Reston, VA is $21.08, according to ZipRecruiter salary data. Most workers in this role earn between $16.78 and $23.27 per hour, depending on experience, location, and employer.

What does a freelance AI data annotator do?

A Freelance AI Data Annotation job involves labeling, tagging, or categorizing data to train artificial intelligence models. This can include tasks like identifying objects in images, transcribing audio, or classifying text. Annotators follow specific guidelines to ensure consistency and accuracy so that the AI can learn from high-quality, well-labeled datasets. These jobs are typically remote, flexible, and can be project-based, making them popular for freelancers. Attention to detail and the ability to follow instructions are key skills for success in this role.

What skills and qualifications are needed to be a freelance AI data annotator?

To thrive as a Freelance AI Data Annotator, you need strong attention to detail, basic computer literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Experience with annotation platforms, labeling tools, and sometimes knowledge of spreadsheet software or project management systems is typically required. Reliability, time management, and effective communication are standout soft skills for meeting project deadlines and collaborating with remote teams. These skills are crucial for ensuring high-quality, accurate data annotations that directly impact the performance of AI models.

What are common challenges faced by freelance AI data annotators, and how can they be addressed?

Freelance AI data annotators often encounter challenges such as maintaining consistency in labeling, meeting tight deadlines, and managing repetitive tasks. To address these, it's important to thoroughly understand the project guidelines, seek clarification from clients when needed, and use annotation tools efficiently. Regular communication with project managers and participating in quality checks can also help ensure accuracy and smooth workflow. Building a routine and taking short breaks can reduce fatigue and improve focus.

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

AspectFreelance Ai Data AnnotationFreelance Data Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexibleRemote, flexible
Industry UsageAI and machine learning projectsData organization and categorization
Job FocusAnnotating data for AI trainingLabeling data for various purposes

Freelance Ai Data Annotation involves preparing data specifically for AI models, often requiring understanding of annotation tools. Freelance Data Labeler may perform similar tasks but can include broader data labeling roles. Both roles are remote, flexible, and essential for data-driven industries, but Ai Data Annotation is more specialized for AI development projects.

What are popular job titles related to Freelance Ai Data Annotation jobs in Reston, VA?

For Freelance Ai Data Annotation jobs in Reston, VA, the most frequently searched job titles are:

What job categories do people searching Freelance Ai Data Annotation jobs in Reston, VA look for?

The top searched job categories for Freelance Ai Data Annotation jobs in Reston, VA are:

What cities near Reston, VA are hiring for Freelance Ai Data Annotation jobs?

Cities near Reston, VA with the most Freelance Ai Data Annotation job openings:

Freelance Ai Data Annotation jobs near you

Project Perseus | Data Quality Analyst - Spanish Speakers (Human-in-the-Loop AI)

Washington, DC

Welocalize
Translation Services • 1 - 5K employees
Learn more about Welocalizeexternal

$38/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 15 days ago

Welocalize rating

6.5

Company rating: 6.5 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

Job description

Data Quality Analyst

Welo Data is looking for experienced, detail-oriented professionals to join our team as Data Quality Analysts. This role sits at the center of execution and quality — bridging Data Labeling Associates (DLAs) and Team Leads to ensure work is not only completed, but done right.

You'll work closely with both people and AI systems — auditing outputs, supporting day-to-day execution, and helping teams apply guidelines correctly in fast-moving, real-world scenarios. The work combines data quality, light project coordination, and hands-on training, where your ability to guide others and think critically is just as important as your own output.

Project Details

  • Job Title: Data Quality Analyst
  • Hiring in: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston
  • Hours: Full-time, 40 hours per week
  • Employment Type: W2 Full-Time Employee
  • Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
  • Pay Rate: $38/hour
  • Contract Duration: 1-year contract with possibility of extension

Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston. Please only apply if you meet this location requirement.

What You'll Do

  • Support quality and execution across DLA teams, ensuring work meets defined standards at scale
  • Audit DLA outputs and provide structured, actionable feedback to improve accuracy and consistency
  • Act as the first line of support for DLAs — answering questions and helping interpret guidelines
  • Help DLAs navigate ambiguity and apply evolving instructions effectively
  • Support onboarding and training of new DLAs through hands-on guidance and coaching
  • Monitor workflows, queues, and blockers — escalating risks and gaps to Team Leads
  • Identify patterns, recurring issues, and edge cases in both human and model outputs
  • Participate in calibrations, team discussions, and stakeholder syncs
  • Contribute to improving guidelines, processes, and overall team performance
  • Document findings and feedback in a clear, concise, and actionable way

What We're Looking For

  • Native-level language proficiency and a university degree (Bachelor's or higher).
  • B2 or superior level of English.
  • 2–4 years of experience in data annotation, content quality, QA, or related fields
  • Strong ability to interpret and apply complex guidelines with consistency
  • Excellent attention to detail with a high bar for quality
  • Ability to stay consistent while working with evolving guidelines and priorities
  • Experience in AI/ML data workflows or human-in-the-loop evaluation environments
  • Prior experience auditing or reviewing the work of others
  • Familiarity with safety, compliance, or policy-driven content evaluation

Benefits

  • Paid Vacation: 6 days
  • Paid Company Holidays: 2 days (Memorial Day and Labor Day)
  • Paid Sick Leave: accrued per applicable state law and company policy
  • Medical, Dental, and Vision Insurance (eligibility applies)
  • Health Savings Account (HSA)
  • 401(k) Retirement Plan
  • Employee Assistance Program
  • Additional voluntary benefits (life, accident, critical illness, etc.)
  • Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes.
  • Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix.
  • Unique Campus Features: Some locations include roof-top nature parks
  • Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks.

$38 - $38 an hour Why This Role This is an opportunity to move beyond traditional data work and play a direct role in how AI systems are evaluated and improved. The work is fast-moving, collaborative, and increasingly central to how modern AI systems are built and deployed.

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