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Annotation Labelling information

What is annotation labelling?

Annotation labelling is the process of tagging or marking data—such as images, text, or audio—with relevant information or labels. This is an essential step in preparing datasets for machine learning and artificial intelligence models, as it helps algorithms understand and learn from raw data. Annotation labelling can include tasks like identifying objects in photos, transcribing speech, or categorizing text. Skilled annotators ensure accuracy and consistency to improve model performance. People in this role often use specialized tools or software to streamline and standardize the annotation process.

What are the key skills and qualifications needed to thrive as an Annotation Labelling Specialist, and why are they important?

To thrive as an Annotation Labelling Specialist, you need strong attention to detail, data analysis capabilities, and familiarity with data annotation standards, usually supported by a background in computer science or related fields. Proficiency with annotation tools such as Labelbox, CVAT, or Supervisely, and sometimes knowledge of basic programming or scripting, is typically required. Excellent communication, consistency, and the ability to follow complex instructions are crucial soft skills for producing high-quality labeled data. These skills ensure the accuracy and reliability of datasets, which are foundational for successful machine learning and AI model development.

What are some common challenges faced by Annotation Labelling professionals, and how can they be managed?

Annotation Labelling professionals often encounter challenges such as maintaining high accuracy while handling repetitive data, meeting tight deadlines, and adapting to evolving project guidelines. To manage these, it’s important to develop strong attention to detail, regularly communicate with team leads to clarify instructions, and leverage annotation tools efficiently. Collaborating closely with quality assurance teams can also help identify and correct errors early, ensuring consistently high-quality outputs.

What is the difference between Annotation Labelling vs Data Labeling Specialist?

AspectAnnotation LabellingData Labeling Specialist
CredentialsBasic technical skills, attention to detailSimilar skills, sometimes additional domain knowledge
Work EnvironmentData annotation platforms, remote or officeData annotation tasks, often remote or in-office
Industry UsageAI, machine learning, autonomous vehiclesAI, machine learning, healthcare, retail
Search & ComparisonCommonly compared for entry-level data tasksRelated but broader role

Annotation Labelling involves marking data such as images, text, or videos to train AI models. Data Labeling Specialists perform similar tasks but may have a broader scope, including verifying and managing labeled data. Both roles are essential in AI development, often overlapping in skills and work environment, but Annotation Labelling is more focused on the annotation process itself.

What are popular job titles related to Annotation Labelling jobs in Washington? For Annotation Labelling jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Annotation Labelling jobs in Washington look for? The top searched job categories for Annotation Labelling jobs in Washington are:
What cities in Washington are hiring for Annotation Labelling jobs? Cities in Washington with the most Annotation Labelling job openings:
Annotation Labelling jobs near you
Project Perseus \u007C Data Labeling Associate - Portuguese (Portugal) Speakers (Human-in-the-Loop A

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

Welo Data

Washington, DC

$34/hr

Full-time

Posted 26 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.