Position Summary

We are seeking a highly skilled Computational Linguist with native proficiency in Hungarian to ensure linguistic precision and quality in our language technology systems. This specialist will serve as the crucial link between advanced speech technologies and Hungarian linguistic expertise, with primary focus on phonetic modeling, grammatical analysis, and comprehensive system evaluation.

Role and Responsibilities

Linguistic Framework Development Design and implement language-specific processing rules and develop user-facing linguistic components for Hungarian language applications.

Phonetic Resources & Lexicon Management Create high-quality phonetic transcriptions using international standards (IPA) and build comprehensive lexical databases and linguistic corpora tailored to Hungarian language characteristics.

Quality Assurance & Model Assessment Conduct rigorous qualitative and quantitative evaluations of system performance across Text-to-Speech (TTS), Automatic Speech Recognition (ASR), and Machine Translation (MT) modules, ensuring adherence to Hungarian grammatical norms and idiomatic expressions.

Language-Specific Analysis Investigate and document Hungarian-specific linguistic phenomena including phonological patterns, morphological structures, and syntactic constructions to enhance model performance and accuracy.

Skills and Qualifications:

Language Proficiency Native speaker of Hungarian with expert-level command of the language's structural properties, dialectal variations, and contemporary usage patterns.

Academic Background Bachelor's or Master's degree in Linguistics, Computational Linguistics, Natural Language Processing, or Computer Science with specialization in language technologies.

Technical Competencies Demonstrated experience with linguistic annotation platforms, phonetic analysis tools (such as Praat), and proficiency in applying International Phonetic Alphabet (IPA) standards for transcription work.

Analytical Capabilities
Strong attention to detail with the ability to detect and diagnose nuanced linguistic inconsistencies that may affect end-user experience and system reliability.

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