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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

$195.8K

How much do data annotation manager jobs pay per year?

As of Sep 5, 2026, the average yearly pay for data annotation manager in Seattle, WA is $110,616.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,200.00 and $142,900.00 per year, depending on experience, location, and employer.

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.

What are the key skills and qualifications needed to thrive as a data annotation manager?

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 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 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:

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

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

Data Annotation Manager jobs near you
Infographic showing various Data Annotation Manager job openings in Seattle, WA as of August 2026, with employment types broken down into 83% Full Time, and 17% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $110,616 per year, or $53.2 per hour.

QA Reviewer & Project Coordinator (On-Site - Seattle or Boston)

mpathic

Seattle, WA • On-site

Full-time

Posted 27 days ago

Job description

About the Role

We're seeking an experienced QA Reviewer & Project Coordinator. This is a hands-on role that combines expert evaluation and quality assurance with day-to-day project coordination. You'll spend most of your time reviewing work, testing AI systems, and ensuring high-quality outputs while also helping keep projects organized, on schedule, and running smoothly.


Position Overview

This position is ideal for someone who enjoys both deep subject matter expertise and operational execution.

You'll serve as one of the primary reviewers for AI safety work, conducting quality reviews, identifying behavioral edge cases, testing AI models through role play and red teaming, and ensuring consistency across projects. In addition, you'll coordinate portions of active programs by tracking deliverables, supporting staffing and project workflows, communicating with stakeholders, and helping identify operational risks before they become issues.


The ideal candidate enjoys solving ambiguous problems, has exceptional attention to detail, communicates clearly, and thrives in a fast-moving startup environment.


What You'll Do


QA & AI Evaluation

  • Review AI content for accuracy, safety, empathy, and policy compliance.
  • Conduct quality assurance reviews of expert evaluations and annotations.
  • Participate in calibration sessions to ensure reviewer consistency and quality.
  • Help improve QA workflows, reviewer documentation, and operational processes.
  • Role play realistic clinical scenarios with AI systems to evaluate behavior across diverse situations.
  • Perform red teaming to identify failure modes, safety risks, and behavioral edge cases.
  • Develop and refine evaluation rubrics, behavioral taxonomies, personas, and scoring guidelines.
  • Document model inconsistencies, safety concerns, and opportunities for improvement.
  • Collaborate with researchers and engineers to improve AI behavior through structured clinical feedback.
  • Maintain strict confidentiality while working with sensitive clinical content.


Project Coordination

  • Support day-to-day execution of AI safety and evaluation projects.
  • Track project timelines, deliverables, and reviewer assignments.
  • Coordinate review queues and help balance workloads across project teams.
  • Monitor project progress and proactively identify risks, blockers, or quality issues.
  • Maintain project trackers, documentation, and reporting dashboards.
  • Assist with staffing coordination as project needs evolve.
  • Help facilitate project meetings and document action items and follow-up tasks.


Required Qualifications

  • Familiarity with ChatGPT, Claude, Gemini, or other large language models.
  • Excellent written communication and documentation skills.
  • Strong organizational skills with exceptional attention to detail.
  • Comfortable managing multiple priorities simultaneously.
  • Ability to work independently while collaborating effectively across teams.
  • Comfortable working in a fast-paced startup environment with evolving priorities.


Preferred Qualifications

  • Experience reviewing or auditing clinical work for quality.
  • Experience with AI safety, LLM evaluation, prompt engineering, or red teaming.
  • Background in trust & safety, content moderation, or behavioral policy development.
  • Experience developing evaluation rubrics, taxonomies, or annotation guidelines.
  • Familiarity with data annotation or human-in-the-loop evaluation workflows.
  • Clinical experience working with serious mental illness, crisis intervention, or complex behavioral health populations.
  • Project coordination or project management experience.
  • Experience leading calibration sessions or reviewer training.
  • Experience with Google Workspace, Slack, spreadsheets, and project management tools.


You'll Thrive Here If You...

  • Notice details that others miss.
  • Can identify quality issues before they become larger problems.
  • Enjoy both analytical review work and coordinating people and projects.
  • Communicate clearly across clinical and operational teams.
  • Balance independent problem solving with knowing when to escalate.
  • Thrive in ambiguity and rapidly changing environments.
  • Care deeply about building AI systems that are safe, trustworthy, and clinically responsible.
  • Take ownership and consistently follow through.


Additional Requirements

  • Ability to work on-site at our designated office location (Seattle or Boston).
  • Willingness to sign comprehensive confidentiality and NDA agreements.
  • Comfortable working with sensitive mental health and AI safety content.
  • Participation in recurring project meetings, calibration sessions, and operational planning.
  • Availability to support occasional high-priority project deadlines as needed, including potential evening and weekend work.


Apply Even If You Don't Check Every Box

We know great candidates might not fit every bullet on a job description. If this role speaks to you and you're excited to help improve the future of healthcare research and AI safety, we'd love to hear from you.

Apply