Remote European Portuguese Linguistic Expert – Contract

Confidential; États-Unis; À distance; USD 60 000 - 110 000; Plein temps

Résumé du poste

Confidential is seeking experienced European Portuguese Linguistic Experts to join a cutting-edge AI research project in partnership with a leading research lab.

This role centers on evaluating how effectively a large language model (LLM) performs in European Portuguese-language tasks and on curating high-quality Portuguese linguistic data to train and improve the model's understanding and generation capabilities.

Qualifications

• Native or near-native fluency in European Portuguese, with excellent command over grammar, syntax, and stylistics.
• Professional experience in translation, editing, linguistic annotation, or NLP data work in Portuguese.
• Strong understanding of European Portuguese nuances, regional variations, register, and context-appropriate usage, including how it differs from Brazilian Portuguese.
• Professional fluency in English.

Responsabilités

• Evaluate the quality and accuracy of LLM-generated Portuguese text across various use cases (e.g., translation, summarization, dialogue, grammar correction).
• Edit, refine, or rewrite Portuguese content to meet linguistic and cultural standards for Portugal.
• Annotate Portuguese text for grammatical, syntactic, and semantic features.
• Identify model errors and language gaps, and collaborate with AI researchers to improve LLM performance.
• Provide detailed linguistic feedback and analysis to guide ongoing model training efforts.

Connaissances

European Portuguese fluency

English proficiency

Attention to detail

Remote collaboration

Description du poste

Confidential is seeking experienced European Portuguese Linguistic Experts to join a cutting-edge AI research project in partnership with a leading research lab.

This role centers on evaluating how effectively a large language model (LLM) performs in European Portuguese-language tasks and on curating high-quality Portuguese linguistic data to train and improve the model's understanding and generation capabilities.