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Data Annotation Project Manager Jobs in Colorado

Data Center Project Manager

Denver, CO ยท 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 ...

Data Center Project Manager

Denver, CO ยท 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 ...

Machine Learning Engineer

Denver, CO ยท Remote

$50 - $70/hr

... annotation, data quality, or evaluation systems. * Master's Degree or PhD Why Join Us: * Competitive pay and flexible remote work. * Collaborate with a team working on cutting-edge AI projects.

Sr Data Center Project Manager

Denver, CO ยท On-site

$124K/yr

Proven ability to manage complex rip-and-replace or migration projects. * Excellent customer relationship, communication, and documentation skills. * Data center lingo and operational fluency ...

New

Data Center COE Project Manager

Denver, CO ยท Remote

$123K/yr

... project manager with paralleling switchgear, protection and control systems, electrical energy management systems, or electrical field services in the data center or power generation/distribution ...

Project Manager

Almont, CO ยท Remote

$60 - $65/hr

The role will drive data modernization, architecture-led projects, integrations, and technical debt ... The PM will manage plans, RAID, financials, and reporting with a focus on clear communication and ...

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Showing results 1-20

Data Annotation Project Manager information

What is the average salary for a data annotation project manager?

The average salary for a data annotation project manager typically ranges from $70,000 to $110,000 annually, depending on experience, location, and company size. In regions with a high cost of living, such as California, salaries tend to be higher to compensate for living expenses.

Does data annotation really pay you?

Data annotation project managers oversee labeling tasks and typically earn a salary or hourly wage, depending on the employer and project scope. Compensation varies based on experience, location, and the complexity of the annotation work, but it is generally a paid role with standard employment benefits. Freelance or contract annotators may be paid per task or project.

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.

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 project managers oversee this work, ensuring accuracy and quality using tools like labeling platforms. The process is legitimate and widely used in industry for creating reliable datasets.

What is the highest salary of data annotation?

The highest salaries for data annotation project managers can reach up to $80,000 to $100,000 annually, depending on experience, location, and the complexity of projects managed. Senior roles with extensive oversight or specialized skills in tools like labeling platforms may earn higher compensation. Salary ranges vary widely based on industry and company size.

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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Software Engineer (C#) - Internal Tooling

Alignerr

Denver, CO โ€ข Remote

Other

Posted 8 days ago


Job description

Software Engineer (C#) - Internal Tooling (AI Infrastructure)
About the Role
What if your C# expertise could directly shape the infrastructure powering the next generation of AI? We're looking for experienced full-stack C# engineers to build and improve the systems that leading AI labs depend on - from data annotation pipelines to evaluation harnesses and quality control tooling.
This is a fully remote contract role with flexible commitment. You'll work on real production systems alongside data, research, and engineering teams at the frontier of AI development.
  • Organization
    : Alignerr
  • Type
    : Hourly Contract
  • Location
    : Remote
  • Commitment
    : 20-40 hours/week
What You'll Do
  • Design, build, and optimize high-performance C# 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 existing C# codebases
  • Collaborate with data, research, and engineering teams to support model training and evaluation workflows
  • Identify and resolve bottlenecks and edge cases in data and system behavior with scalable solutions
  • Participate in synchronous design reviews to iterate on architecture and implementation decisions
  • Build and maintain robust benchmarking and performance evaluation harnesses
Who You Are
  • 3-5+ years of professional experience writing production-grade C#
  • Strong full-stack developer background with solid systems programming fundamentals
  • Expertise in interoperability scenarios - such as invoking Python ML models from .NET or wrapping native libraries
  • Experienced designing robust harnesses for benchmarking and evaluating system performance
  • Native or fluent English speaker with clear written and verbal communication skills
  • Able to commit 20-40 hours per week with reliability and consistency
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 with or alongside research teams in a fast-moving environment
Why Join Us
  • Work on cutting-edge AI projects alongside leading research labs
  • Fully remote and flexible - work when and where it suits you
  • Freelance autonomy with the structure of meaningful, high-impact technical work
  • Build systems that directly influence how next-generation AI models are trained and evaluated
  • Potential for ongoing work and contract extension as new projects launch