Spanish (Spain) Data Labeler — Onsite AI Training Data

Welocalize, Inc.; Seattle (WA); Vor Ort; USD 32.000 - 45.000; Vollzeit

Benefits dieser Stelle

Paid vacation 6 days

Paid holidays 2 days

Sick leave accrual

Medical insurance

Dental insurance

Vision insurance

Health Savings Account

401(k) retirement plan

Employee assistance program

Voluntary benefits

Free meals on site

Snacks & beverages

Shuttle service

Bike-to-work

Zusammenfassung

Welocalize, Inc. is seeking a Data Labeling Analyst for the Project Perseus team to support Spanish (Spain) speech and language data.

This onsite role focuses on producing high-quality labeled data and structured feedback to AI models, with strong emphasis on consistency and accuracy. Responsibilities include evaluating model outputs, auditing data, following guidelines, and collaborating with cross-functional teams to improve evaluation frameworks.

Qualifikationen

• Native-level fluency in Spanish (Spain).
• Strong written communication and language fundamentals.
• 1 year of work experience in data labeling, annotation, or content-focused work; or a Bachelor's degree or equivalent.

Aufgaben

• Evaluate AI model outputs and provide structured, high-quality feedback.
• Perform audit-based reviews of data and model behavior, identifying patterns and edge cases.
• Apply guidelines consistently and flag deviations from real-world scenarios.
• Contribute to improving evaluation frameworks beyond execution.
• Identify trends in model performance and communicate insights clearly.
• Participate in team calibrations and stakeholder discussions.
• Collaborate with leads to refine quality standards and document findings clearly.

Kenntnisse

Spanish fluency (Spain)

Attention to detail

Written communication

Time management

Ausbildung

Bachelor's degree or equivalent

Jobbeschreibung

Welocalize, Inc. is seeking a Data Labeling Analyst for the Project Perseus team to support Spanish (Spain) speech and language data.

This onsite role focuses on producing high-quality labeled data and structured feedback to AI models, with strong emphasis on consistency and accuracy. Responsibilities include evaluating model outputs, auditing data, following guidelines, and collaborating with cross-functional teams to improve evaluation frameworks.