Work Type
Remote
Location
Global
Compensation
Not specified
About this opportunity
Overview
CNTXT AI is hiring a fully remote, hourly contractor to support AI data and language projects on a flexible, project-based schedule. Work may include data annotation, LLM evaluation, content generation, and localization QA across a range of topics and use cases.
The role calls for senior-level Italian localization and translation judgment, along with minimum C1 English proficiency, to help ensure terminology, tone, and cultural nuance are accurate and consistent.
What You'll Do
- Label, classify, and structure documents, tables, and other content for AI training datasets.
- Review AI-generated responses for accuracy, reasoning quality, coherence, and cultural or linguistic appropriateness.
- Write high-quality prompts and model responses, and at times record voice samples to support AI learning.
- Check localized outputs for terminology, tone, cultural nuance, and locale-specific details such as units, references, names, and dates.
- Identify meaning drift, ambiguity, locale inconsistencies, and subtle errors, then explain corrections clearly in writing.
- Fact-check localized content using reliable sources and consistent reasoning.
- Spot reasoning gaps, methodological errors, and unclear explanations even when the language itself is fluent.
Requirements
- Bachelor's degree or higher, preferably in Translation, Linguistics, Localization, Communications, or a related field.
- Native or near-native Italian proficiency with strong writing and editing skills.
- Minimum C1 English proficiency for reading, writing, and interpreting prompts, source materials, and evaluation guidelines.
- Strong editorial judgment around register, tone, punctuation, inclusivity, and cultural nuance.
- Ability to work independently, communicate clearly, and maintain consistent quality across time zones.
Preferred Experience
- Professional localization or translation experience.
- Previous experience with AI data training, annotation, or evaluation.
- Familiarity with MQM/LQA concepts, including severity, category, and root-cause thinking.
- Familiarity with CAT tools and QA workflows.
