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. At Innodata, we're working with the world's largest technology companies on the next generation of generative AI and large language models (LLMs). We are seeking a Mathematics Subject Matter Expert with exceptional writing and analytical skills to join our AI/ML data team. In this role, you will help train advanced Large Language Models (LLMs) by producing, curating, and reviewing high-quality educational and technical mathematics content. Create high-quality, accurate, and pedagogically sound content in mathematics, including:
Research abstracts and technical writing
Review and refine mathematics-related model outputs
Identify and fill conceptual gaps in LLM training data
Build datasets across various mathematics domains (e.g., Algebra, Calculus, Geometry, Linear Algebra, Probability, Statistics, Number Theory, Discrete Math, etc.)
Provide feedback to improve LLM performance in reasoning, solving, and communicating mathematical content
Master's or PhD in Mathematics or a closely related discipline (MS with exceptional experience may be considered)
Strong background in theoretical and/or applied mathematics
Excellent technical and academic writing skills
As part of the project, you are required to complete the English language assessment.
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