Catalan Language Expert – AI Data Annotation

iMerit Inc.; España; Remote; EUR 25,000 - 55,000; Part time

Benefits offered by this job

Flexible hours

Fully remote

Freelance contract

Job summary

iMerit is seeking fluent Catalan speakers to help build privacy-safe training data for AI. Work is remote, freelance with flexible hours, so you can fit it around other commitments.

You will read Catalan text, listen to audio, and annotate PII, transcribe segments with timestamps, and label images with bounding boxes according to project guidelines. Prior annotation experience is a plus, but training is provided.

Qualifications

• Fluent or near-fluent Catalan with strong reading and listening skills.
• Strong English reading and writing (B2) to follow guidelines in English.
• Excellent attention to detail for tagging rules and privacy-sensitive data.

Responsibilities

• Read Catalan text and tag PII according to guidelines.
• Listen to Catalan audio and timestamp PII segments.
• Review transcripts and mark PII within text with accuracy.
• Draw bounding boxes around objects or regions in images and apply labels.

Skills

Catalan fluency

English B2

Attention to detail

Annotation tools

Data confidentiality

Tools

Annotation tools

Job description

Remote | Freelance | Flexible Hours

iMerit Scholars brings together skilled professionals from around the world to help build safer, more accurate and more culturally aware AI. Our experts work on real AI projects for leading technology companies, shaping how AI systems understand and respond in their language.

About the Project

We're looking for fluent Catalan speakers to help build privacy-safe training data for AI systems. You'll identify and tag personally identifiable information (PII), such as names, addresses, phone numbers and ID numbers, across Catalan text, audio recordings and transcripts, and label visual content in images using bounding boxes. This is fully remote, flexible work that you can fit around your existing commitments.

What You'll Do

• Read Catalan text and tag all personally identifiable information (names, addresses, phone numbers, email addresses, ID and account numbers, dates of birth and similar details) according to project guidelines
• Listen to raw Catalan audio recordings and mark the segments where PII is spoken, with accurate timestamps
• Review Catalan audio transcript snippets and tag PII within the transcribed text, checking against the audio where needed
• Draw precise bounding boxes around specified objects or regions in images (for example, visible text, documents, faces or other PII) and apply the correct labels
• Apply tagging categories consistently and handle ambiguous or edge cases as defined in the guidelines
• Flag unclear audio, poor-quality images or content that falls outside the guidelines

What We're Looking For

• Fluent or near-fluency in Catalan, with strong reading and listening comprehension, including natural, informal and fast-paced speech
• Strong English reading and writing skills (B2 level or above), as guidelines and instructions are in English
• Excellent attention to detail and the ability to follow detailed tagging guidelines consistently
• Comfort working with annotation tools for text, audio and images
• An understanding of what counts as personal or sensitive information, and respect for data confidentiality
• A reliable computer, good-quality headphones and a stable internet connection

Nice to Have

• Familiarity with regional variation across Catalan-speaking areas (e.g. Central Catalan, Valencian, Balearic and North-Western varieties, and usage in Catalonia, Valencia, the Balearic Islands and Andorra)
• Comfort with Catalan–Spanish code-switching, which often appears in everyday speech and text
• Previous experience with data annotation, PII/data redaction, transcription or bounding-box labelling
• Background in transcription, translation, proofreading, data entry or content moderation

What We Offer

• The chance to help build safer, privacy-respecting AI for Catalan speakers
• Access to future projects on the iMerit Scholars platform