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Flexible Data Annotation Tech Jobs in Seattle, WA

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Flexible Data Annotation Tech information

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$13

$25

$39

How much do flexible data annotation tech jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for flexible data annotation tech in Seattle, WA is $26.00, according to ZipRecruiter salary data. Most workers in this role earn between $19.13 and $30.91 per hour, depending on experience, location, and employer.

What is a flexible data annotation tech?

Flexible Data Annotation Tech jobs involve labeling, categorizing, or tagging data—such as images, text, audio, or video—to help train machine learning models. These roles are often remote or offer flexible schedules, making them appealing for those seeking adaptable work hours. Tasks can include identifying objects in photos, transcribing audio, or sorting information based on specific guidelines. The work is essential for improving the accuracy of artificial intelligence systems by providing them with high-quality annotated data. No advanced technical skills are usually required, but attention to detail and reliability are important.

What are the key skills and qualifications needed to thrive as a flexible data annotation tech, and why are they important?

To thrive as a Flexible Data Annotation Tech, you need attention to detail, accuracy, and a basic understanding of data labeling or annotation processes, often requiring at least a high school diploma. Familiarity with annotation platforms, data labeling tools, and productivity software is typically necessary, and experience with machine learning datasets can be advantageous. Strong time management, communication, and adaptability help you excel in collaborative and ever-changing project environments. These skills ensure high-quality, consistent data output that directly impacts the performance of AI and machine learning systems.

What are some common challenges faced by flexible data annotation techs, and how can they be addressed?

Flexible Data Annotation Techs often encounter challenges such as maintaining consistency across large volumes of data, adapting to evolving project guidelines, and managing tight deadlines. To address these challenges, it's important to establish clear communication with project leads, regularly review annotation protocols, and utilize available training resources. Building strong attention to detail and staying organized can also help ensure high-quality outputs and job satisfaction.

What is the difference between Flexible Data Annotation Tech vs Data Labeler?

AspectFlexible Data Annotation TechData Labeler
CredentialsBasic computer skills, training in annotation toolsBasic education, sometimes specific software training
Work EnvironmentRemote or on-site, tech-focusedPrimarily remote or on-site, data processing settings
Industry UsageAI, machine learning, data scienceAI, machine learning, data preparation
Job FocusApplying labels to datasets using annotation toolsLabeling data according to guidelines

Flexible Data Annotation Tech roles involve using specialized tools to annotate datasets for AI training, often requiring some technical training. Data Labelers focus on applying labels to data, typically with less technical complexity. Both roles are essential in AI development but differ mainly in technical requirements and scope.

Can I do data annotation with no experience?

Data annotation roles often do not require prior experience, as training is typically provided to teach specific labeling tools and guidelines. Basic computer skills and attention to detail are usually sufficient to start, making it accessible for beginners. Over time, developing familiarity with annotation software and understanding data types can improve efficiency and accuracy.

Do data annotation jobs offer flexible hours?

Data annotation jobs often offer flexible hours, allowing workers to choose when they complete tasks, especially in freelance or remote roles. However, some positions may have specific deadlines or part-time schedules depending on the employer or platform used. Flexibility can vary based on the company's policies and project requirements.

What are the most commonly searched types of Data Annotation Tech jobs in Seattle, WA?

The most popular types of Data Annotation Tech jobs in Seattle, WA are:

What are popular job titles related to Flexible Data Annotation Tech jobs in Seattle, WA?

For Flexible Data Annotation Tech jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Flexible Data Annotation Tech jobs in Seattle, WA look for?

The top searched job categories for Flexible Data Annotation Tech jobs in Seattle, WA are:

Flexible Data Annotation Tech jobs near you

Project Perseus \u007C Data Labeling Associate - Portuguese (Brazil) Speakers (Human-in-the-Loop AI)

Welo Data

Redmond, WA • On-site

$34/hr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 14 days ago

Job description

Overview

Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate.

This is not a traditional annotation role.

You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines.

We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders.

Project Details

  • Job Title: Data Labeling Associate
  • 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: $34/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: New York City, 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
  • Evaluate AI model outputs and provide structured, high-quality feedback
  • Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes
  • Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios
  • Contribute to improving evaluation frameworks, not just executing them
  • Identify trends in model performance and communicate insights clearly
  • Participate in team discussions, calibrations, and stakeholder syncs
  • Partner with leads and cross-functional teams to refine quality standards
  • Document findings 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.  
  • 1–2 years of professional writing experience with strong, structured writing skills
  • Ability to apply complex writing rules and guidelines consistently
  • Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience
  • Strong critical thinking and attention to detail
  • Ability to make sound judgment calls in ambiguous situations
  • Naturally curious about AI, technology, and how systems behave
  • Comfortable speaking up, asking questions, and contributing ideas
  • Strong written and verbal communication skills
  • Ability to stay consistent while working with evolving guidelines
  • Experience in data quality, QA, annotation, or analysis is helpful — but not required
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.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Working at Welo Data

What to expect from working at Welo Data

From Welo Data

About Welo Data, in their own words

From Welo Data

Welo Data is a global AI data services company powering the next generation of AI. We build, annotate, and validate the training datasets that make AI models accurate, safe, and ready for the real world — across languages, cultures, and domains.

Our team of experts spans the globe, combining deep technical knowledge with a human-centered approach. If you want your work to shape how AI understands the world, you'll find your place here.

Diversity and inclusion statement

From Welo Data

Our Strength is derived from Winning Together. Welo Data is unequivocally committed to developing and fostering a workplace and organizational culture that values the diversity of thought and perspective delivered by a diverse global workforce operating within an inclusive organization.