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Data Annotation Spanish Jobs (NOW HIRING)

Xai

AI Tutor - Spanish

Manhattan, NY · On-site

BASIC QUALIFICATIONS Native proficiency in Spanish with exposure to diverse accents, dialects, or ... Experience working with speech/audio datasets, annotation workflows, or AI training data, including ...

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Data Annotation Spanish information

What are the key skills and qualifications needed to thrive as a Data Annotation Specialist (Spanish), and why are they important?

To thrive as a Data Annotation Specialist (Spanish), you need proficiency in the Spanish language, attention to detail, and a basic understanding of data labeling concepts, often supported by a high school diploma or higher. Familiarity with annotation tools such as Labelbox, Supervisely, or proprietary platforms is typically required, along with knowledge of file management systems. Strong communication, consistency, and the ability to work independently are important soft skills for excelling in this position. These skills ensure accurate, high-quality data labeling that directly impacts the performance of AI and machine learning models.

What are some common challenges faced by Data Annotation Specialists working with Spanish language data, and how can they be addressed?

Data Annotation Specialists working with Spanish language data often encounter challenges such as managing regional dialects, idiomatic expressions, and cultural nuances that can affect the accuracy of annotations. To address these challenges, it's important to have a strong understanding of the specific dialect or variant required by the project and to consistently refer to established guidelines or glossaries. Collaboration with team members and regular quality checks help ensure consistency and high-quality output, while ongoing training can keep annotators updated on best practices and new annotation tools.

What is Data Annotation in Spanish?

Data Annotation in Spanish refers to the process of labeling or tagging data—such as text, audio, or images—in the Spanish language to make it understandable for machine learning models. This work helps train artificial intelligence systems to recognize and process Spanish language content accurately. Data annotators may categorize content, transcribe audio, or highlight specific elements in images or texts. The quality of annotated data directly impacts the performance of language models and other AI applications.

What is the difference between Data Annotation Spanish vs Data Labeling Specialist?

AspectData Annotation SpanishData Labeling Specialist
CredentialsBasic computer skills, attention to detailSimilar, often requires familiarity with labeling tools
Work EnvironmentRemote or office-based, tech companiesRemote or on-site, tech and AI industries
Industry UsageUsed in AI training for Spanish language dataUsed across various industries for data preparation
Search IntentLooking for Spanish-specific annotation rolesSearching for general data labeling jobs

Data Annotation Spanish focuses on annotating data specifically in Spanish, often requiring language skills. Data Labeling Specialist is a broader role involving labeling data in various formats and languages. Both roles are essential in AI development, but Data Annotation Spanish is specialized for Spanish language datasets.

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Infographic showing various Data Annotation Spanish job openings in the United States as of May 2026, with employment types broken down into 5% As Needed, 79% Full Time, 1% Part Time, 14% Contract, and 1% Nights. Highlights an 46% Physical, 1% Hybrid, and 53% Remote job distribution.
Project Perseus | Data Quality Analyst - Spanish Speakers (Human-in-the-Loop AI)

Project Perseus | Data Quality Analyst - Spanish Speakers (Human-in-the-Loop AI)

Welo Data

Austin, TX • On-site

Full-time

Posted 4 days ago

Job description

Job Summary:
Welo Data is looking for experienced, detail-oriented professionals to join our team as Data Quality Analysts. This role sits at the center of execution and quality — bridging Data Labeling Associates (DLAs) and Team Leads to ensure work is not only completed, but done right.
Responsibilities:
• Support quality and execution across DLA teams, ensuring work meets defined standards at scale
• Audit DLA outputs and provide structured, actionable feedback to improve accuracy and consistency
• Act as the first line of support for DLAs — answering questions and helping interpret guidelines
• Help DLAs navigate ambiguity and apply evolving instructions effectively
• Support onboarding and training of new DLAs through hands-on guidance and coaching
• Monitor workflows, queues, and blockers — escalating risks and gaps to Team Leads
• Identify patterns, recurring issues, and edge cases in both human and model outputs
• Participate in calibrations, team discussions, and stakeholder syncs
• Contribute to improving guidelines, processes, and overall team performance
• Document findings and feedback in a clear, concise, and actionable way
Qualifications:
Required:
• Native-level language proficiency and a university degree (Bachelor’s or higher)
• B2 or superior level of English
• 2–4 years of experience in data annotation, content quality, QA, or related fields
• Strong ability to interpret and apply complex guidelines with consistency
• Excellent attention to detail with a high bar for quality
• Ability to stay consistent while working with evolving guidelines and priorities
• Experience in AI/ML data workflows or human-in-the-loop evaluation environments
• Prior experience auditing or reviewing the work of others
• Familiarity with safety, compliance, or policy-driven content evaluation
• Must be authorized to work in the U.S. (no visa sponsorship)
• This is a 100% onsite position — remote work is not available for this role
• Candidates must be located in or able to commute to one of the specified cities
Company:
With 27+ years of experience, Welo Data is the human-centered infrastructure for globally effective AI. Founded in , the company is headquartered in , , with a team of 1001-5000 employees. The company is currently Late Stage.

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