Infrastructure Engineering - Linguist III

Location: US - NY - Remote

Duration: 8 months with possible extension

Main duties: Perform linguistic analyses on large datasets. Perform linguistic error analysis of AI model outputs, determining what the most frequent and severe error categories are. Write and revise guidelines for human annotation and other AI projects, including but not limited to translation tasks. Conduct typological and sociolinguistic research on a large number of languages, highlighting their similarities and differences. Perform linguistic analyses for Responsible AI (toxic language, hate speech, gender bias and other cultural biases) in massively multilingual settings. Conduct linguistic literature reviews on various NLP-adjacent topics, and summarize findings. Compare the quality of deliveries between vendors, identify error patterns, and provide actionable feedback. Provide information or guidance relative to any aspect of linguistic knowledge (typology, morpho-syntax, sociolinguistics, classification, phonetics/phonology, pragmatics, etc.).Reach out to and collaborate with native speakers in various languages. Communicate results of linguistic analyses to engineers and research scientists.

Skills: Must have strong written and spoken communication skills, especially business and research communication. Must be near-native proficient in one of the two following languages: German or Turkish and must have a high level of proficiency (CEFRL C1) in the other one of the two previously mentioned languages. Working knowledge in other languages is a plus. Proficiency in a low-resource language is valued. Must be able to code in Python (must) and query databases using SQL, other coding languages used for data analysis are a plus. Must be able to independently work through complex requests and perform under pressure. Strong ability to work independently, prioritize, plan, and track work, as well as report progress education or training in the basics of project management is a plus self-motivation is a must. Working knowledge of international language-classification standards is valued.

Education: Graduate degree in Linguistics or related field is a must; PhD is a plus. A background or specialization in corpus linguistics is a plus. Experience with field work is a plus. A graduate degree in Literature or English is not an appropriate substitution degree in Computer Science with a specialization in NLP is not an appropriate substitution. Must have a very firm grasp of the following linguistic fields: language typology, syntax, morphology, sociolinguistics (especially dialectology and discourse analysis), corpus linguistics, writing systems, pragmatics, phonology. Must have some experience with applying basic Natural Language Processing techniques.

Years of experience: 0-3Experience working cross-functionally

Important: Preferably no known conflicts of interest in the fields of machine translation, ASR, TTS, or LLM research (as FAIR Linguists need to be contributing to research papers)