1

Data Annotation Services Jobs in Colorado (NOW HIRING)

Review, annotate and analyze data obtained from research in an accurate and timely manner * Adhere ... Review patient charts and images to assist in annotation for training and evaluating AI models.

Review, annotate and analyze data obtained from research in an accurate and timely manner * Adhere ... Review patient charts and images to assist in annotation for training and evaluating AI models.

Data Annotation Services information

What are the key skills and qualifications needed to thrive in Data Annotation Services, and why are they important?

To excel in Data Annotation Services, strong attention to detail, data literacy, and a foundational understanding of data labeling processes are essential, often requiring a high school diploma or equivalent. Familiarity with annotation platforms, labeling tools, and sometimes basic knowledge of scripting or data management systems is typically expected. Strong work ethic, consistency, and effective communication skills help individuals stand out in collaborative, deadline-driven environments. These capabilities ensure high-quality, accurate labeled data, which is critical for training reliable machine learning models.

What is the difference between Data Annotation Services vs Data Labeling Specialists?

AspectData Annotation ServicesData Labeling Specialists
CredentialsTypically no formal credentials required; focus on trainingOften have training in specific tools or industry standards
Work EnvironmentCollaborative, often remote or in-office teamsSimilar, working in teams or independently on labeling tasks
Industry UsageUsed by AI/ML companies for training datasetsEmployed in similar settings, focusing on labeling data for AI models
Search & Comparison IntentUnderstanding services offered for data preparationLooking for roles or tasks related to data labeling

Data Annotation Services encompass the broader process of preparing and annotating data for AI and machine learning projects, often provided by specialized companies. Data Labeling Specialists are individual professionals or team members who perform the actual labeling tasks within these services. While both are closely related, services refer to the overall offering, whereas specialists are the personnel executing the work.

What are some common challenges faced when working in data annotation services, and how can I address them?

In data annotation services, one common challenge is maintaining consistency and accuracy, especially when handling large datasets or ambiguous data points. Clear annotation guidelines and regular communication with team leads help ensure that everyone interprets the data similarly. Additionally, repetitive tasks can lead to fatigue, so it's important to take scheduled breaks and leverage available annotation tools to streamline workflows. Collaborating with peers to discuss edge cases also helps improve overall data quality and fosters a supportive team environment.

What are data annotation services?

Data annotation services involve labeling or tagging data—such as images, text, audio, or video—to make it understandable for machine learning models. These services are essential in training artificial intelligence systems to recognize patterns, objects, or other relevant information in raw data. Companies use data annotation to improve the accuracy and effectiveness of AI applications, such as self-driving cars, chatbots, and image recognition. Professional annotators or specialized platforms often perform these tasks to ensure high-quality, consistent results.
What are popular job titles related to Data Annotation Services jobs in Colorado? For Data Annotation Services jobs in Colorado, the most frequently searched job titles are:
What cities in Colorado are hiring for Data Annotation Services jobs? Cities in Colorado with the most Data Annotation Services job openings:

Software Engineer (C#) - Internal Tooling

Alignerr

Denver, CO • Remote

Other

Posted 5 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