Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale.

We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We are seeking experienced Subject Matter Experts (SMEs) to support the training and evaluation of advanced AI models.

You will leverage your domain expertise to create, review, and validate high-quality technical content, ensuring AI-generated responses are accurate, logical, and scientifically sound. Domains Hiring For High-Energy Physics Theory Number Theory / Competition Mathematics Computer Vision (Classical CV, CNNs, Video Analytics, Segmentation) Statistical Machine Learning (Feature Selection, Statistical Modelling) Natural Language Processing (NLP) with Large Language Models (LLMs) Signal Processing (Compressed Sensing, Digital Signal Processing, Feature Selection) Computational Neuroscience What You'll Own: Review the assigned domain archives thoroughly to understand the subject matter, terminology, processes, policies, historical context, and key updates. Extract relevant facts, concepts, scenarios, exceptions, and decision points from the archived material.

Create clear and accurate question-and-answer pairs based only on the approved archive content. Cover different levels of complexity, including factual recall, conceptual understanding, application-based scenarios, reasoning, and edge cases. Ensure every answer is complete, unambiguous, and traceable to the relevant archive, document, or source section.

Avoid assumptions, external information, duplicate questions, and content that is outdated or inconsistent with the latest archived version. Flag gaps, conflicting information, unclear language, or missing context in the archives for domain-owner clarification. Classify each Q A with relevant metadata such as domain, topic, difficulty level, source archive, version/date, and reviewer status.

You'll Thrive in this Role If You Have: Master's or PhD in the relevant domain Strong research, analytical, and problem-solving skills. Excellent written English communication. Prior experience in research, academia, or industry is preferred.

Familiarity with AI, Machine Learning, or LLMs is an added advantage.

Qualifications

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