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English Language Specialist (Australian-based ) at Perle

, , Australia -

Full Time

Start Date

Immediate

Expiry Date

16 Feb, 26

Salary

0.0

Posted On

18 Nov, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Annotation, Error Identification, Quality Assurance, Feedback, Reporting, Linguistic Quality, Cultural Appropriateness, Grammatical Errors, Syntactic Issues, Semantic Inconsistencies, Transcription Errors, Native-Level Fluency, Linguistics, Translation, Audio Data, Text Data

Industry

technology;Information and Internet

Description

The Opportunity We are seeking a highly skilled and detail-oriented English Speaker with native-level fluency who is based in Australia to evaluate and improve the quality of linguistic data, specifically in text and audio formats, for an AI/Natural Language Processing (NLP) project with perle.ai. What you'll do Data Annotation & Evaluation: Accurately evaluate and annotate large volumes of text and audio data in Ukrainian for linguistic quality, accuracy, clarity, and cultural appropriateness. Error Identification: Identify and categorize grammatical errors, syntactic issues, semantic inconsistencies, transcription errors, and inappropriate or non-native phrasing. Quality Assurance: Ensure the linguistic data adheres to project-specific guidelines and high-quality standards. Feedback & Reporting: Document and report linguistic issues and trends, providing clear, constructive feedback to improve the overall language model performance. Qualifications Native Speaker & Residency: Must be a native-level fluency speaker of English and currently residing in Australia. Education: Bachelor's Degree (minimum) from an accredited institution. Linguistic Background (Preferred): A degree in Linguistics, Hebrew Language Studies, Translation, or a related field is highly preferred.

Responsibilities

The role involves evaluating and annotating text and audio data for linguistic quality and accuracy. Additionally, the specialist will identify errors and provide feedback to enhance the language model's performance.