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Annotation Tech information

What are Annotation Techs?

Annotation Techs, short for Annotation Technicians, are professionals who label, categorize, and tag data—such as images, text, or audio—to help train machine learning models. Their work is critical in fields like artificial intelligence, where high-quality, accurately labeled data is needed to teach algorithms how to recognize patterns and make decisions. Annotation Techs may use specialized software tools to identify objects in images, transcribe speech, or classify pieces of text. Attention to detail and consistency are key skills in this role, as errors or inconsistencies can affect the performance of AI systems. These professionals often work in teams and may collaborate with data scientists and engineers to ensure data quality.

How hard is it to get hired by data annotation?

Getting hired as an annotation technician typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible, though competition can vary based on the employer and location.

What is the difference between Annotation Tech vs Data Labeler?

AspectAnnotation TechData Labeler
Required CredentialsHigh school diploma or equivalent; some roles may prefer technical certificationsHigh school diploma or equivalent; minimal certifications needed
Work EnvironmentOffice or remote; using specialized annotation toolsOffice or remote; using basic labeling software
Industry UsageAI, machine learning, autonomous vehicles, healthcareAI, machine learning, data preparation

Annotation Tech and Data Labeler roles often overlap in data preparation for AI projects. Annotation Tech typically involves more specialized tools and may require some technical knowledge, whereas Data Labelers focus on basic labeling tasks. Both roles are essential in training AI systems, but Annotation Tech positions often demand a deeper understanding of annotation processes and tools.

What does an annotation job do?

An annotation job involves labeling or tagging data, such as images, text, or videos, to help train machine learning models. Annotation technicians use specialized tools to add accurate labels, which are essential for developing AI systems, and often require attention to detail and knowledge of data privacy. The work is typically performed in a digital environment with flexible schedules and may require basic technical skills.

What are the key skills and qualifications needed to thrive as an Annotation Tech, and why are they important?

To thrive as an Annotation Tech, you need strong attention to detail, data labeling proficiency, and familiarity with data annotation guidelines, often supported by a background in computer science or related fields. Experience with annotation platforms such as Labelbox, Supervisely, or CVAT, and sometimes knowledge of basic scripting or data formats like JSON and XML, is typically required. Excellent communication, problem-solving skills, and the ability to follow complex instructions set top performers apart. These skills ensure high-quality, accurate data labeling that directly impacts the effectiveness of machine learning models.

Is annotation tech legit?

Annotation tech refers to roles involving labeling and annotating data for machine learning and AI training. These jobs are generally legitimate and often involve tasks like image, text, or audio annotation using specialized tools, with some positions offering flexible schedules and remote work. However, job seekers should verify the employer's reputation and avoid scams by researching the company before applying.

What are some common challenges faced by Annotation Techs when working with large datasets?

Annotation Techs often work with large and diverse datasets, which can present challenges such as maintaining consistency and accuracy across annotations, especially when dealing with ambiguous or complex data. Additionally, the repetitive nature of the work can lead to fatigue, making it important to stay focused and adhere to established guidelines. Collaboration with data scientists and project managers is crucial to clarify requirements and address any uncertainties, ensuring that the annotated data meets project standards and deadlines.

Does data annotation tech really pay?

Data annotation technicians typically earn hourly wages that range from minimum wage to around $15-$20 per hour, depending on experience, location, and the complexity of tasks. Some companies offer bonuses or pay increases for specialized skills or certifications, but overall, pay is generally modest compared to other tech roles.
What are popular job titles related to Annotation Tech jobs in Chicago, IL? For Annotation Tech jobs in Chicago, IL, the most frequently searched job titles are:
What job categories do people searching Annotation Tech jobs in Chicago, IL look for? The top searched job categories for Annotation Tech jobs in Chicago, IL are:
Annotation Tech jobs near you
Project Perseus \u007C Data Labeling Associate -Turkish Speakers (Human-in-the-Loop AI)

Project Perseus \u007C Data Labeling Associate -Turkish Speakers (Human-in-the-Loop AI)

Welo Data

Chicago, IL • On-site

$34/hr

Full-time

Posted 8 days ago

Be an early applicant

Job description

Overview

Welo Data is looking for sharp, curious, and detail-oriented individuals to join our team as Data Labeling Associate.

This is not a traditional annotation role.

You’ll be working directly with cutting-edge AI systems — evaluating outputs, identifying gaps, and helping improve how these systems behave in real-world scenarios. The work sits at the intersection of data quality, model evaluation, and human judgment, where your ability to think critically matters just as much as following guidelines.

We’re looking for people who are naturally curious about AI, comfortable forming opinions, and confident in contributing to conversations with teammates, leads, and stakeholders.

Project Details

  • Job Title: Data Labeling Associate
  • Hiring in: NYC, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston
  • Hours: Full-time, 40 hours per week
  • Employment Type: W2 Full-Time Employee
  • Work Authorization: Must be authorized to work in the U.S. (no visa sponsorship)
  • Pay Rate: $34/hour
  • Contract Duration: 1-year contract with possibility of extension
Important: This is a 100% onsite position — remote work is not available for this role. To be considered, candidates must be located in or able to commute to one of the following cities: New York City, Seattle, Bellevue, Redmond, San Francisco, Sunnyvale, Burlingame, Austin, Los Angeles, Washington DC, Chicago, Boston. Please only apply if you meet this location requirement.
What You’ll Do
  • Evaluate AI model outputs and provide structured, high-quality feedback
  • Perform audit-based reviews of data and model behavior — identifying patterns, edge cases, and failure modes
  • Apply guidelines thoughtfully — and flag when they don’t reflect real-world scenarios
  • Contribute to improving evaluation frameworks, not just executing them
  • Identify trends in model performance and communicate insights clearly
  • Participate in team discussions, calibrations, and stakeholder syncs
  • Partner with leads and cross-functional teams to refine quality standards
  • Document findings in a clear, concise, and actionable way
What We’re Looking For
  • Native-level language proficiency and a university degree (Bachelor’s or higher).
  • B2 or superior level of English.  
  • 1–2 years of professional writing experience with strong, structured writing skills
  • Ability to apply complex writing rules and guidelines consistently
  • Strong understanding of safety considerations in GenAI data delivery, with 2+ years of relevant experience
  • Strong critical thinking and attention to detail
  • Ability to make sound judgment calls in ambiguous situations
  • Naturally curious about AI, technology, and how systems behave
  • Comfortable speaking up, asking questions, and contributing ideas
  • Strong written and verbal communication skills
  • Ability to stay consistent while working with evolving guidelines
  • Experience in data quality, QA, annotation, or analysis is helpful — but not required
Benefits
  • Paid Vacation: 6 days
  • Paid Company Holidays: 2 days (Memorial Day and Labor Day)
  • Paid Sick Leave: accrued per applicable state law and company policy
  • Medical, Dental, and Vision Insurance (eligibility applies)
  • Health Savings Account (HSA)
  • 401(k) Retirement Plan
  • Employee Assistance Program
  • Additional voluntary benefits (life, accident, critical illness, etc.)
  • Free Gourmet Food: Free breakfast, lunch, and dinner are provided, featuring a wide variety of cuisines in multiple cafes.
  • Micro-kitchens & Snacks: Offices are stocked with free snacks and beverages, including premium coffee and La Croix.
  • Unique Campus Features: Some locations include roof-top nature parks
  • Commuter Benefits: Free transport, shuttles, and sometimes bike-to-work perks.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Working at Welo Data

What to expect from working at Welo Data

From Welo Data

About Welo Data, in their own words

From Welo Data

Welo Data is a global AI data services company powering the next generation of AI. We build, annotate, and validate the training datasets that make AI models accurate, safe, and ready for the real world — across languages, cultures, and domains.

Our team of experts spans the globe, combining deep technical knowledge with a human-centered approach. If you want your work to shape how AI understands the world, you'll find your place here.

Diversity and inclusion statement

From Welo Data

Our Strength is derived from Winning Together. Welo Data is unequivocally committed to developing and fostering a workplace and organizational culture that values the diversity of thought and perspective delivered by a diverse global workforce operating within an inclusive organization.