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Data Annotation Project Manager Jobs in Redmond, WA

YO AI Labs

AI Finance Expert - Remote

Seattle, WA · Remote

$100 - $200/hr

... project at the intersection of finance and artificial intelligence . In this role, you will help ... Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ...

New

YO AI Labs

AI Finance Expert - Remote

Seattle, WA · Remote

$100 - $200/hr

... project at the intersection of finance and artificial intelligence . In this role, you will help ... Data Annotation * Problem-Solving * Independent Research * Attention to Detail Preferred ...

New

H5 Data Centers

Data Center Project Manager

Seattle, WA · On-site

$125K - $130K/yr

Provide Management status of all active Data Center projects and Key Performance indicators. * Lead internal resources and vendors in the execution of Data Center deployments and builds.

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All JobsData Annotation Project JobsData Annotation Project Manager JobsData Annotation Project Manager Jobs in Redmond, WA

Data Annotation Project Manager information

See Redmond, WA salary details

$18

$64

$89

How much do data annotation project manager jobs pay per hour?

As of Aug 14, 2026, the average hourly pay for data annotation project manager in Redmond, WA is $64.41, according to ZipRecruiter salary data. Most workers in this role earn between $55.72 and $75.38 per hour, depending on experience, location, and employer.

How much do data annotation project managers make?

Data annotation project managers typically earn between $60,000 and $100,000 annually, depending on experience, location, and company size. They oversee annotation teams, coordinate workflows, and ensure quality standards are met, often requiring familiarity with annotation tools and project management skills.

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

To thrive as a Data Annotation Project Manager, you need strong project management skills, a solid understanding of data annotation processes, and experience with quality assurance, often supported by a degree in a relevant field. Familiarity with annotation tools (like Labelbox or Supervisely), workflow management platforms, and sometimes agile or PMP certification is highly beneficial. Exceptional communication, attention to detail, and leadership abilities help you effectively coordinate teams and ensure project deliverables meet quality standards. These skills are essential for managing complex annotation projects efficiently, maintaining data integrity, and supporting successful machine learning outcomes.

What are some common challenges faced by data annotation project managers, and how can they be managed effectively?

One of the primary challenges Data Annotation Project Managers face is ensuring high-quality, consistent labeling across large and sometimes distributed annotation teams. Managing tight deadlines while maintaining annotation accuracy requires effective training, clear guidelines, and regular quality checks. Additionally, balancing communication between data scientists, clients, and annotators is crucial to align expectations and resolve ambiguities quickly. Successful managers often implement robust feedback loops, leverage annotation tools with built-in quality control features, and foster an open environment for continuous improvement.

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

AspectData Annotation Project ManagerData Labeling Specialist
CredentialsTypically requires project management experience, certifications in data management or related fieldsOften requires basic technical skills, familiarity with labeling tools, sometimes certifications in data annotation
Work EnvironmentOversees teams, manages projects, coordinates workflows in office or remote settingsPerforms labeling tasks, often in a remote or on-site environment, focused on data tagging
Employer & Industry UsageUsed by tech companies, AI firms, and data service providers for managing annotation projectsEmployed within similar industries, focusing on executing labeling tasks under supervision

The main difference is that the Data Annotation Project Manager oversees and coordinates annotation projects, ensuring quality and deadlines, while the Data Labeling Specialist focuses on executing the labeling tasks themselves. Both roles are essential in the data annotation process but differ in responsibilities and scope.

What is a data annotation project manager?

A Data Annotation Project Manager is responsible for overseeing projects that involve labeling and categorizing data, such as images, text, or audio, to train machine learning models. They coordinate teams of annotators, manage project timelines, and ensure the quality and accuracy of the annotated data. This role often acts as a bridge between data scientists, clients, and annotation teams, ensuring project requirements are met efficiently and effectively.

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

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

What cities near Redmond, WA are hiring for Data Annotation Project Manager jobs?

Cities near Redmond, WA with the most Data Annotation Project Manager job openings:

Data Annotation Project Manager jobs near you
Infographic showing various Data Annotation Project Manager job openings in Redmond, WA as of August 2026, with employment types broken down into 88% Full Time, 11% Part Time, and 1% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution, with an average salary of $133,965 per year, or $64.4 per hour.

AI Finance Expert - Remote

YO AI Labs

Seattle, WA • Remote

$100 - $200/hr

Part-time

Posted 3 days ago

New

Job description

Job Title: AI Finance Domain Expert

Job Type: Contractor (Part-Time)
Location: Remote

Job Overview

We are seeking experienced AI Finance Domain Experts to contribute their financial expertise to an innovative project at the intersection of finance and artificial intelligence. In this role, you will help improve next-generation AI systems by reviewing, evaluating, and refining AI-generated financial content. No prior AI experience is required—your financial expertise, analytical skills, and professional judgment are what matter most.

Key Responsibilities

  • Analyze, review, and edit AI-generated financial content for accuracy, clarity, and relevance.

  • Develop, refine, and evaluate prompts related to financial analysis, valuation, and investment decision-making.

  • Assess and annotate financial data, reports, and AI-generated outputs using structured evaluation criteria.

  • Author and review investment memos, due diligence reports, research summaries, and technical documentation.

  • Evaluate AI outputs for logical consistency, factual accuracy, and adherence to professional financial standards.

  • Conduct independent research and fact-checking to validate financial information.

  • Provide detailed feedback to improve AI model performance and financial reasoning.

Required Skills

  • Critical Thinking

  • Analytical Reasoning

  • Quality Assurance

  • Prompt Engineering

  • AI Output Evaluation

  • Financial Analysis

  • Technical & Report Writing

  • Business Communication

  • Content Review & Editing

  • Fact Checking

  • Data Interpretation

  • Data Annotation

  • Problem-Solving

  • Independent Research

  • Attention to Detail

Preferred Qualifications

  • Minimum 3 years of experience in private equity, venture capital, investment banking, equity research, corporate development, investment management, or strategic finance.

  • Experience preparing investment memos, valuation analyses, financial models, due diligence reports, or market research.

  • Strong analytical, critical thinking, and problem-solving skills.

  • Excellent written communication and professional editing abilities.

  • Experience with prompt authoring, AI output evaluation, data annotation, or content review is a plus.

  • Master's, MBA, JD, PhD, or another advanced degree is preferred.

  • Commitment to producing high-quality, accurate, and well-documented work.