Innodata (NASDAQ: INOD) is a leading data engineering company. With more than 2,000 customers and operations in 13 cities around the world, we are an AI technology solutions provider-of-choice for 4 out of 5 of the world’s biggest technology companies, as well as leading companies across financial services, insurance, technology, law, and medicine.
By combining advanced machine learning and artificial intelligence (ML/AI) technologies, a global workforce of subject matter experts, and a high-security infrastructure, we’re helping usher in the promise of AI. Innodata offers a powerful combination of both digital data solutions and easy-to-use, high-quality platforms.
Our global workforce includes over 5,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany.
About the role:
We are seeking a highly analytical and detail-oriented linguist to support AI training initiatives and linguistic content creation. This role is ideal for someone with a strong academic background in linguistics (syntax, semantics, pragmatics, morphology, phonology, sociolinguistics, etc.) and a passion for language, technology, and clear communication. You will play a crucial role in shaping the capabilities of large language models (LLMs) and NLP-based systems through high-quality linguistic data curation, annotation, and evaluation.
Job Title: Linguistics Expert – AI Training & Content Writing
Experience Level: Master's or PhD in Linguistics or a related field
Key Responsibilities:
* Create or edit linguistically- rich content including grammar guides, syntactic analyses, usage explanations, or examples for NLP pipelines.
* Identify and resolve issues related to ambiguity, bias, and grammaticality.
* Perform quality assurance (QA) on model outputs for fluency, tone, factual accuracy, and language appropriateness.
* Annotate linguistic datasets with syntactic, semantic, or pragmatic labels.
* Support internal teams by conducting linguistic research and summarizing findings.
* Apply linguistic knowledge to evaluate model behavior, error patterns, and generalization issues.
Qualifications:
* Master’s or PhD in Linguistics, Applied Linguistics, Computational Linguistics, or a related field.
* Deep understanding of linguistic theory and language structure.
* Experience with one or more of the following is a plus: computational linguistics, corpus analysis, language data annotation, LLM training.
* Strong writing, editing, and communication skills.