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Data Annotation Services Jobs in Colorado (NOW HIRING)

Data Annotation Services information

How hard is it to get hired by data annotation?

Getting hired for data annotation services typically requires basic computer skills, attention to detail, and the ability to follow instructions. Many positions are entry-level and may not require prior experience, but familiarity with annotation tools and good accuracy can improve chances of employment.

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.

Does data annotation actually pay you?

Data annotation services typically pay workers for labeling data used in machine learning models. Payment rates vary depending on the platform, task complexity, and experience, with many jobs offering hourly or per-task compensation. Reliable platforms often require basic skills in data handling and attention to detail.

Is data annotation real or fake?

Data annotation is a legitimate job that involves labeling data such as images, text, or videos to train machine learning models. It requires attention to detail and familiarity with annotation tools, and it is widely used in AI development. The work is real and essential for creating accurate AI systems.

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 does a data annotation job do?

A data annotation job involves labeling or tagging data such as images, text, or videos to help train machine learning models. Workers use tools to add metadata, which improves the accuracy of AI systems, often working remotely with flexible schedules and requiring attention to detail. Knowledge of annotation tools and data quality standards is beneficial.

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.
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Principal Python Engineer - ML Infrastructure

Alignerr

Denver, CO • On-site

Other

Posted 2 days ago


Job description

Principal Python Engineer - ML Infrastructure (AI Training)
About the Role
What if your Python expertise could directly shape the infrastructure powering some of the world's most advanced AI systems? We're looking for a Principal Python Engineer to build and optimize the data pipelines, annotation tooling, and evaluation systems that leading AI labs depend on - working on real production code with meaningful, measurable impact.
This is a fully remote, flexible contract role for a senior engineer who thrives at the intersection of systems programming, distributed computing, and AI infrastructure.
  • 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 large-scale AI data pipelines and model evaluation workflows
  • Develop full-stack backend tooling and services for data annotation, validation, and quality control at scale
  • Diagnose and resolve bottlenecks across compute-heavy, distributed systems using advanced async patterns and profiling techniques
  • Improve reliability, safety, and performance across existing production Python codebases
  • Collaborate closely with data, research, and engineering teams to accelerate model training and evaluation cycles
  • Drive architectural decisions through synchronous design reviews and clear technical communication
Who You Are
  • 5+ years writing production Python for large-scale infrastructure or platform engineering
  • Deep expertise in distributed computing, concurrency, and advanced asynchronous programming patterns
  • Fluent in Python internals - including GIL limitations, memory profiling, and performance optimization for compute-heavy workloads
  • Experienced full-stack developer with a strong systems programming background
  • Clear, confident communicator capable of driving technical strategy and architectural decisions
  • 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
  • Background in distributed systems architecture or developer tooling
  • Exposure to working directly with AI research teams or labs
Why Join Us
  • Work on real, high-impact production systems used by leading AI research labs
  • Fully remote and flexible - work when and where it suits you
  • Freelance autonomy with the depth and structure of meaningful, long-term technical work
  • Collaborate with top engineers and researchers at the frontier of AI development
  • Potential for ongoing work and contract extension as new projects launch