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Data Annotation Manager Jobs in Seattle, WA (NOW HIRING)

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Data Annotation Manager information

See Seattle, WA salary details

$35.3K

$110.6K

$195.7K

How much do data annotation manager jobs pay per year?

As of Jun 11, 2026, the average yearly pay for data annotation manager in Seattle, WA is $110,553.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,100.00 and $142,800.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a Data Annotation Manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and proficiency with annotation tools and team management can influence pay levels.

Is data annotation high paying?

Data annotation managers typically earn higher salaries than entry-level annotators due to their supervisory responsibilities and expertise in labeling tools and processes. Salaries vary based on experience, location, and company size, but the role generally offers competitive pay within the data labeling industry.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation managers oversee this work, ensuring accuracy and quality using tools like labeling platforms and quality control procedures.

What are some common challenges faced by Data Annotation Managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What does a Data Annotation Manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

Is it hard to get a job with data annotation?

Securing a job as a data annotation manager typically requires experience in data labeling, familiarity with annotation tools, and understanding of data quality standards. While entry-level roles may be accessible with basic skills, advancing to managerial positions often demands relevant experience and leadership abilities.

What are the key skills and qualifications needed to thrive as a Data Annotation Manager, and why are they important?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

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

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in Seattle, WA? The most popular types of Data Annotation jobs in Seattle, WA are:
What are popular job titles related to Data Annotation Manager jobs in Seattle, WA? For Data Annotation Manager jobs in Seattle, WA, the most frequently searched job titles are:
Infographic showing various Data Annotation Manager job openings in Seattle, WA as of June 2026, with employment types broken down into 100% Full Time. Highlights an 74% In-person, and 26% Remote job distribution, with an average salary of $110,553 per year, or $53.2 per hour.
Project Perseus | Speech & Voice AI Analyst - Turkish Speakers

Project Perseus | Speech & Voice AI Analyst - Turkish Speakers

Welo Data

Seattle, WA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Job Summary:
Welo Data is looking for detail-oriented and reliable individuals to join their team as Speech & Voice AI Analysts. The role focuses on executing high-volume data labeling and annotation tasks across speech and voice datasets to support AI systems, ensuring accuracy and consistency in data handling.
Responsibilities:
• Execute high-volume data labeling and annotation tasks across speech and voice datasets
• Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale
• Work with audio and language data, including transcription, categorization, and tagging
• Maintain strong throughput while meeting quality expectations
• Escalate unclear or ambiguous cases appropriately
• Adapt to evolving guidelines and workflows as systems and requirements change
• Support baseline data production needs for AI training pipelines
• Contribute to team calibrations and quality alignment sessions
Qualifications:
Required:
• Native-level fluency in Turkish
• Strong written communication skills and language fundamentals
• 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field.
• Ability to follow detailed instructions and apply guidelines consistently
• High attention to detail and ability to maintain accuracy in repetitive tasks
• Comfort working in structured, process-driven environments
• Ability to manage time effectively and maintain steady output
• Willingness to ask questions and escalate when needed
Preferred:
• Basic familiarity with AI, speech technology, or language data is a plus
Company:
With 27+ years of experience, Welo Data is the human-centered infrastructure for globally effective AI. Founded in , the company is headquartered in , , with a team of 1001-5000 employees. The company is currently Late Stage.

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.