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Data Annotation Project Manager Jobs in Colorado

HDR

Data Center Project Manager

Denver, CO · On-site

$124K/yr

HDR is looking for a Data Center Project Manager to join our Building Engineering Services team in Denver, Colorado. Our team is looking for a candidate to meet the demands of our client base ...

HDR

Data Center Project Manager

Denver, CO · On-site

$124K/yr

HDR is looking for a Data Center Project Manager to join our Building Engineering Services team in Denver, Colorado. Our team is looking for a candidate to meet the demands of our client base ...

HDR

Data Center Project Manager

Denver, CO · On-site

$124K/yr

HDR is looking for a Data Center Project Manager to join our Building Engineering Services team in Denver, Colorado. Our team is looking for a candidate to meet the demands of our client base ...

CompuNet, Inc.

Project Manager - Data Center

Denver, CO · On-site

$124K/yr

They are seeking a Project Manager to join their Data Center Practice, responsible for managing data center projects that meet client-defined objectives and support the growth of CompuNet's services.

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Data Annotation Project Manager information

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 using tools like labeling platforms and project management software.

Does data annotation actually pay?

Data annotation project managers oversee tasks where annotators are paid for labeling data used in machine learning. The pay for annotators varies depending on the platform, project complexity, and experience, with many earning hourly wages or per-task rates. The role of a project manager involves coordinating these efforts and ensuring quality, often with a salary or contract-based compensation.

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

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.

How hard is it to get hired by data annotation?

Getting hired as a data annotation project manager typically requires relevant experience in project management, familiarity with annotation tools, and strong organizational skills. The role often involves coordinating teams and ensuring quality standards, with some positions requiring certifications or prior experience in data labeling environments. Competition varies depending on the company and location, but demonstrating technical knowledge and management ability can improve chances of hiring.

What is the salary of data annotation manager?

The salary of a Data Annotation Project Manager typically ranges from $60,000 to $100,000 annually, depending on experience, location, and company size. They often oversee teams using annotation tools and ensure quality standards are met in data labeling projects.

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.
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Data Annotation Project Manager jobs near you

Python Software Engineer - AI Workflows

Alignerr

Denver, CO • On-site

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Python Software Engineer - AI Workflows
About the Role
What if your Python expertise could directly shape the infrastructure powering the next generation of AI? We're looking for senior full-stack Python engineers to design and build the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on to train and improve their models.
This is a fully remote contract role working on real production systems - not toy projects. If you're a sharp engineer who wants to work at the frontier of AI development, this is the role for you.
  • Organization
    : Alignerr
  • Type
    : Hourly Contract
  • Location
    : Remote
  • Commitment
    : 20-40 hours/week
What You'll Do
  • Design, build, and optimize high-performance Python systems supporting AI data pipelines and evaluation workflows
  • Develop full-stack tooling and backend services for large-scale data annotation, validation, and quality control
  • Improve reliability, performance, and safety across production Python codebases
  • Integrate AI services and APIs with robust error handling and edge case coverage
  • Identify bottlenecks and failure modes in data and system behavior, then implement scalable solutions
  • Collaborate with data, research, and engineering teams to support model training and evaluation workflows
  • Participate in synchronous design reviews to iterate on system architecture and implementation decisions
Who You Are
  • 3-5+ years of professional experience writing production-grade Python
  • Strong full-stack developer with a solid systems programming background
  • You write clean, maintainable code and naturally reach for linters, formatters, and comprehensive test coverage
  • Experienced gluing together AI services and APIs with confidence - you anticipate edge cases before they bite
  • Clear, direct communicator - both in writing and in technical discussions
  • Native or fluent English speaker
  • Available to commit 20-40 hours per week
Nice to Have
  • Prior experience with data annotation, data quality, or evaluation systems
  • Familiarity with AI/ML workflows, model training, or benchmarking pipelines
  • Experience with distributed systems or developer tooling
  • Background working directly with AI labs or research teams
Why Join Us
  • Work on cutting-edge AI projects alongside leading research labs - real systems, real impact
  • Fully remote and async-friendly - work from wherever you do your best work
  • Freelance autonomy with the structure of meaningful, technically challenging projects
  • Contribute directly to the infrastructure that shapes how next-generation AI models are built and evaluated
  • Potential for ongoing work and contract extension as new projects launch

Apply