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QA Machine Learning

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.

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