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Full Time Data Annotation Tech Jobs (NOW HIRING)

GE HealthCare

Data Manager - AI Development

Waukesha, WI · On-site

... annotation activities, and ensures data readiness, traceability, and compliance throughout the AI ... Please note - this is a full time, onsite role located in Waukesha, WI. Roles and Responsibilities ...

Amazon

Catalog Specialist, RCX

Bellevue, WA · On-site

Key job responsibilities • Work closely with our product, technology, and science teams to support Machine Learning (ML) models • Perform data annotation required to train and evaluate ML models ...

Gehc

Data Manager - AI Development

Waukesha, WI · On-site

... annotation activities, and ensures data readiness, traceability, and compliance throughout the AI ... Please note - this is a full time, onsite role located in Waukesha, WI. Roles and Responsibilities ...

TSMG

AI/ML Data Contributor

Jackson, MS

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

TSMG

AI/ML Data Contributor

Fargo, ND · Remote

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

TSMG

AI/ML Data Contributor

Philadelphia, PA

Previous experience in data annotation, QA, or testing * Interest in AI, machine learning, or emerging technologies What We Offer * Paid, flexible task-based work * Opportunity to work on innovative ...

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How much do full time data annotation tech jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for full time data annotation tech in the United States is $22.84, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $27.16 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Data Annotation Tech, and why are they important?

To thrive as a Full Time Data Annotation Tech, you need strong attention to detail, basic data management skills, and familiarity with data labeling practices, typically supported by a high school diploma or equivalent. Experience with annotation tools (such as Labelbox, Supervisely, or similar platforms) and basic proficiency in spreadsheet or database systems are commonly required. Reliability, consistency, and effective communication are crucial soft skills for quality assurance and collaboration with data teams. These skills and qualities are essential to ensure the accuracy and efficiency of annotated datasets, which directly impact the performance of machine learning models.

How does a Full Time Data Annotation Tech typically collaborate with data scientists and engineers on projects?

As a Full Time Data Annotation Tech, you will regularly work alongside data scientists and engineers to ensure the accuracy and quality of labeled datasets used for machine learning models. Collaboration often involves attending project meetings to clarify annotation guidelines, providing feedback on ambiguous data cases, and updating annotation processes based on team input. Clear communication is essential, as your work directly impacts model performance and downstream analytics. This team-oriented environment fosters learning and provides insight into broader AI development workflows.

What are Full Time Data Annotation Techs?

Full Time Data Annotation Techs are professionals responsible for labeling and categorizing data used to train machine learning models. They examine various types of data, such as images, text, or audio, and apply specific tags or annotations according to project guidelines. Their work is essential in ensuring the accuracy of artificial intelligence systems by providing high-quality, structured datasets. Full-time positions typically involve working standard business hours and may require familiarity with specialized annotation tools and attention to detail.

What is the difference between Full Time Data Annotation Tech vs Data Labeling Specialist?

AspectFull Time Data Annotation TechData Labeling Specialist
CredentialsBasic computer skills, attention to detailSimilar credentials, often with training in labeling tools
Work EnvironmentOffice or remote, collaborative teamsRemote or on-site, focused on labeling tasks
Industry UsageAI, machine learning, tech companiesAI, autonomous vehicles, healthcare
Job FocusAnnotating data for machine learning modelsLabeling data to improve AI accuracy

Both roles involve data annotation and labeling, often requiring similar skills and working environments. The main difference lies in job titles used by employers and the scope of responsibilities, with 'Full Time Data Annotation Tech' emphasizing a broader technical role, while 'Data Labeling Specialist' may focus more on specific labeling tasks.

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Full Time Data Annotation Tech jobs near you
AI Evaluation & Annotation Specialist (Entry-Mid Level) - Japanese (USA)

AI Evaluation & Annotation Specialist (Entry-Mid Level) - Japanese (USA)

Volga Partners

Manhattan, NY • On-site

$12 - $16/hr

Full-time

PTO

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

Job description

Are you curious, detail-oriented, and excited about shaping the future of artificial intelligence? We're looking for AI Evaluation & Annotation Specialists to help train and improve Large Language Models (LLMs). In this role, you'll review AI-generated responses, provide corrections, evaluate quality, and follow structured guidelines to ensure accuracy and consistency.

No engineering background required — if you enjoy problem-solving, analyzing language, and following structured tasks, this role is a great fit. This is a hands-on, production-based role where accuracy, focus, and consistency matter. What You'll Do Review AI-generated responses and rate them for clarity, correctness, and relevance Annotate and label content based on project-specific guidelines Follow detailed written instructions and apply them consistently Generate or evaluate prompts depending on assignment type Work with QA Leads to apply feedback and continuously improve task quality Report completed work daily and meet productivity and quality standards What Makes You a Strong Fit detail-oriented and enjoy accuracy-based work You can follow instructions carefully and apply them consistently You are comfortable working independently with minimal supervision You have strong reading comprehension and critical thinking skills You communicate clearly and respond to feedback professionally Experience with annotation, evaluation, translation, linguistics, or QA is helpful, but not required — training and guidance is provided.

Schedule & Work Expectations This role is aligned with specific QA and project schedules. You must be available during one of the below time windows depending on your language team: Language Shift Window (IST) 06:30–15:30 IST or 19:30–03:30 IST Standard expectation: One consecutive 8-hour shift Alternative: Two 4-hour shifts (both must fall fully within the allowed schedule) Only logged, approved hours are paid no paid holidays / no paid time off model. Why Join Work with a global team Entry point into the growing AI and language technology industry Exposure to real-world AI model training Skill development in annotation, QA/evaluation, and structured AI tasks This is a strong opportunity for those looking to grow within AI data work, linguistic evaluation, QA, or model training roles.

Compensation Range Rates vary by language and experience level (L1/L2). Below are current approved ranges in USD: $12.00 USD to $16.00 USD per hour Requirements All applicants are required to complete a short skills-based assessment as part of the selection process. This assessment is unpaid and is used solely to confirm eligibility and alignment with project quality standards.

Completing the assessment does not guarantee selection; however, it is mandatory in order to be considered for this role. Assessment Requirement Fluency in Japanese, with strong written and verbal communication skills Bachelor's degree in Linguistics, Computer Science, or a related field; or equivalent experience Experience or interest in AI, Machine Learning, or data annotation preferred Strong attention to detail and ability to work with complex data sets Familiarity with translation and localization processes is an advantage Ability to work independently and collaboratively in a fast-paced environment Proficient in using Artificial Intelligence and data annotation tools, with a willingness to learn new technologies #J-18808-Ljbffr

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