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Annotation Labelling Jobs in Seattle, WA (NOW HIRING)

Machine Learning Data Linguist, Alexa AI

Seattle, WA · On-site

$130K - $156K/yr

BASIC QUALIFICATIONS - Bachelor's degree or equivalent - Experience in natural language data labeling, data annotation, linguistic annotation or other forms of data markup, as well as leading a team ...

Machine Learning Data Linguist, Alexa AI

Seattle, WA · On-site

$130K - $156K/yr

... Label, generate, and ensure the quality of datasets. - Work closely with ML Data Linguists and scientists to understand data ambiguities and resolve issues in annotation guidelines. - Conduct in ...

Staff Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to datasets without rewriting source data, then versioned, quality-checked, and served, partnering with ...

Staff Data Engineer

Seattle, WA

$130K - $156K/yr

Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to datasets without rewriting source data, then versioned, quality-checked, and served, partnering with ...

... annotation and labeling for ML model training and evaluation - Experience working on the MTurk or Sagemaker platform for data annotation tasks - Understanding of data annotation methodologies and ...

... annotation and labeling for ML model training and evaluation - Experience working on the MTurk or Sagemaker platform for data annotation tasks - Understanding of data annotation methodologies and ...

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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 job categories do people searching Annotation Labelling jobs in Seattle, WA look for? The top searched job categories for Annotation Labelling jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Annotation Labelling jobs? Cities near Seattle, WA with the most Annotation Labelling job openings:
Portuguese (Portugal) Data Labeling Associate((Human-in-the-Loop AI))

Portuguese (Portugal) Data Labeling Associate((Human-in-the-Loop AI))

Welocalize

Seattle, WA • On-site

Full-time

Re-posted 20 days ago


Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

361st of 451 rated business services


Job description

Job Summary:
Welocalize is a company focused on data quality and AI systems, and they are seeking a Data Labeling Associate to join their team. In this role, you will evaluate AI model outputs, provide feedback, and contribute to improving evaluation frameworks while working closely with cross-functional teams.
Responsibilities:
• 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
Qualifications:
Required:
• 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
Preferred:
• Experience in data quality, QA, annotation, or analysis is helpful — but not required
Company:
Welocalize provides translation supply chain management solutions that deliver market-ready, translated content. Founded in 1997, the company is headquartered in Frederick, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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