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Data Annotation Manager Jobs in Colorado (NOW HIRING)

University of Colorado

Research Associate

Aurora, CO · On-site

Review patient charts and images to assist in annotation for training and evaluating AI models ... EMR or EHR and data management systems) * Background understanding of medical AI, particularly in ...

University of Colorado

Research Associate

Aurora, CO · On-site

Review patient charts and images to assist in annotation for training and evaluating AI models ... EMR or EHR and data management systems) * Background understanding of medical AI, particularly in ...

All JobsData Annotation Manager JobsData Annotation Manager Jobs in Colorado

Data Annotation Manager information

See Colorado salary details

$32.6K

$102.1K

$180.9K

How much do data annotation manager jobs pay per year?

As of Jun 25, 2026, the average yearly pay for data annotation manager in Colorado is $102,149.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,400.00 and $132,000.00 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 Colorado? The most popular types of Data Annotation jobs in Colorado are:
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What cities in Colorado are hiring for Data Annotation Manager jobs? Cities in Colorado with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you

Image process Technician

Crox Consulting Inc

Broomfield, CO • On-site

$16 - $17/hr

Contractor

Posted 6 days ago

Job description

This role involves reviewing and analyzing digital images for quality, accuracy, and compliance with established standards. You will play a key role in ensuring the integrity of image data used for applications such as mapping, navigation, AI training, and transportation systems.

Key Responsibilities:

  • Review, evaluate, and tag digital images based on company standards and guidelines.

  • Identify and correct errors, inconsistencies, or anomalies in image content.

  • Maintain high productivity and accuracy levels while working with large volumes of data.

  • Collaborate with quality assurance and data teams to ensure image integrity and proper labeling.

  • Use specialized tools and software to perform image assessments and annotations.

  • Provide feedback and documentation on recurring image issues or system improvements.

  • Meet daily/weekly production targets and performance metrics.

  • Ensure confidentiality and security of sensitive visual data.

Qualifications:

  • High school diploma or GED required; Associate's or Bachelor's degree preferred.

  • 1+ year of experience in a detail-oriented or quality assurance role, ideally in image processing or digital review.

  • Excellent attention to detail and strong visual analysis skills.

  • Comfortable using computers and learning new software tools quickly.

  • Ability to work independently and as part of a team in a fast-paced environment.

  • Strong organizational and time management skills.

Preferred Skills:

  • Experience with GIS systems, image annotation, or machine learning datasets.

  • Familiarity with transportation, navigation, or mapping platforms.

  • Basic understanding of photography, digital imagery, or computer vision is a plus.