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Data Label Jobs (NOW HIRING)

Pumpkin Intelligence, Inc.

Data Labeler

$30K - $50K/yr

Label and categorize model outputs according to internal evaluation frameworks * Moderate sensitive ... Can make consistent decisions across large volumes of data * Enjoys analyzing nuanced situations ...

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Data Label information

What is the difference between Data Label vs Data Annotator?

AspectData LabelData Annotator
Primary RoleAssigns labels to data for machine learning modelsPerforms detailed annotation of data, including labeling and marking specific features
Skills & CertificationsBasic understanding of data types, labeling toolsMore detailed annotation skills, familiarity with annotation tools
Work EnvironmentData labeling platforms, remote or on-siteAnnotation tools, often similar to labeling platforms
Industry UsageUsed across AI, machine learning, and data science projectsUsed in similar fields, often with more complex annotation tasks

Data Label and Data Annotator roles are closely related, with Data Labeling focusing on assigning simple labels to data, while Data Annotators perform more detailed and complex annotations. Both roles are essential in preparing data for AI and machine learning, often using similar tools and working within the same industry environments.

What are some common challenges faced by Data Labelers, and how can they be addressed?

Data Labelers often face challenges such as handling large volumes of repetitive data, maintaining high accuracy under tight deadlines, and quickly adapting to changing labeling guidelines. To address these challenges, it's important to develop strong attention to detail, use quality control processes like regular peer reviews, and communicate proactively with team leads if guidelines are unclear. Additionally, many teams use specialized annotation tools to streamline workflows and minimize errors, making it helpful to become familiar with these platforms.

What are the key skills and qualifications needed to thrive as a Data Labeler, and why are they important?

To thrive as a Data Labeler, you need strong attention to detail, basic computer literacy, and familiarity with data annotation processes, often supported by a high school diploma or equivalent. Experience with labeling platforms, annotation tools, and sometimes knowledge of data management systems is typically required. Reliability, consistency, and the ability to follow precise instructions are the soft skills that set top performers apart. These skills ensure accurate and high-quality data labeling, which is critical for training effective machine learning models.

What are data labelers?

Data labelers are professionals who annotate or tag data—such as images, text, or audio—to provide context and structure for use in machine learning and artificial intelligence projects. Their work involves identifying and labeling key features in raw data so that algorithms can learn to recognize patterns and make predictions. Data labeling is a crucial step in training supervised learning models, ensuring the accuracy and effectiveness of AI systems.
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Data Label jobs near you
Infographic showing various Data Label job openings in the United States as of July 2026, with employment types broken down into 70% Full Time, 10% Part Time, and 20% Contract. Highlights an 100% In-person job distribution.
Finnish Data Labeling Analyst(Speech & Voice )

Finnish Data Labeling Analyst(Speech & Voice )

Welocalize

San Francisco, CA • On-site

Full-time

Re-posted 16 days ago

Welocalize rating

5.9

Company rating: 5.9 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

357th of 449 rated business services

Job description

Job Summary:
Welocalize is looking for detail-oriented and reliable individuals to join their team as Data Labeling Analysts, supporting speech and voice AI systems. This role focuses on building datasets that power AI systems, requiring strong judgment and attention to detail while executing high-volume data labeling tasks.
Responsibilities:
• Execute high-volume data labeling and annotation tasks across speech and voice datasets
• Follow detailed guidelines to ensure consistency, accuracy, and data integrity at scale
• Work with audio and language data, including transcription, categorization, and tagging
• Maintain strong throughput while meeting quality expectations
• Escalate unclear or ambiguous cases appropriately
• Adapt to evolving guidelines and workflows as systems and requirements change
• Support baseline data production needs for AI training pipelines
• Contribute to team calibrations and quality alignment sessions
Qualifications:
Required:
• Native-level fluency in Croatian
• Strong written communication skills and language fundamentals
• 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent academic qualification in a related field.
• Ability to follow detailed instructions and apply guidelines consistently
• High attention to detail and ability to maintain accuracy in repetitive tasks
• Comfort working in structured, process-driven environments
• Ability to manage time effectively and maintain steady output
• Willingness to ask questions and escalate when needed
Preferred:
• Basic familiarity with AI, speech technology, or language data is a plus
Company:
Welocalize provides translation supply chain management solutions that deliver market-ready, translated content. Founded in 1997, the company is headquartered in Frederick, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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