Infrastructure Engineering - Linguist III

Infrastructure Engineering - Linguist III

Location: Remote

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 native speaker of a non-English language (preferably Indonesian) with a high level of proficiency in another Austronesian language, plus broad knowledge of other languages in the same family.
• 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.
• A 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.

Experience:

• Years of experience: 0-3
• Experience working cross-functionally
• Experience collaborating with machine learning, NLP, or software engineers, or data scientists
• Experience contributing to research papers
• 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)