Location: Fully Remote within the U.S. (excluding Alaska, California, Colorado, Montana, Nebraska, Nevada, New York, Washington, Puerto Rico)

Employment Type: Full-Time

Who we are:

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 7,000 employees in the United States, Canada, United Kingdom, the Philippines, India, Sri Lanka, Israel and Germany. We’re poised for a period of explosive growth over the next few years.

About the Role:

At Innodata, we’re partnering with the world’s leading technology companies to build the future of generative AI and large language models (LLMs).

This opportunity designed for those who want to make a real impact. Whether you’re a writer, linguist, educator, researcher, or just deeply passionate about language and logic, this role lets you contribute to cutting-edge AI development.




You’ll be helping LLMs learn the intricacies of language and reasoning—not just how to write, but how to think. If you’ve ever dreamed of shaping the intelligence behind tomorrow’s technology, this is your chance.

This is more than just a job—it’s a rare chance to help shape the future of AI.

What You’ll Be Doing:

Core tasks would include (any/multiple of) but not limited to the following:

• Evaluation: Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.
• Annotation Labeling: Labeling elements of a piece of content rather than the content as a whole.
• Classification: Assigning predefined categories or labels to items.
• Content Quality: Evaluating the perceived quality and/or appropriateness of content
• Content Understanding: Generating labels to advance understanding of a concept, trend etc.
• Data Augmentation: Creation of additional training data for machine learning models by applying transformations to the original data, such as modifying images (rotation, flipping, cropping), generating new text (paraphrasing, summarization), or altering audio/video signals (speed modification, pitch shifting) to reduce overfitting and increase dataset diversity.
• Grading: Reviewing data and identifying whether or not a product feature works as intended based on the project’s guidelines.
• Identification Labeling: Labeling model outputs to identify if a piece of content is or isn’t something. Examples: identify clickbait; identifying gaming videos; identifying branded content.
• Preference Ranking: Ordering or ranking items based on a set of preferences or criteria.
• Prompt Generation: Creating prompts or questions that will be used to generate responses from a language model or other AI system.
• Relevance Evaluation: Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).
• Response Generation: Generating responses to prompts or questions using a language model or other AI system.
• Response Rewrite: Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.
• Response Summarization: Producing concise summaries of longer pieces of text or data.
• Similarity Evaluation: Projects where content is compared in order to drive a determination.
• Transcription: Converting spoken language or audio content into written text.
• Translation: Converting text or spoken language from one language to another.
• Data Collection: Gathering and compiling various forms of data to be used for training, evaluating, or fine-tuning the AI models. This may include text, images, videos, audio files, or other types of digital content.

Minimum Qualifications:

• A Bachelor’s degree or higher is required. Advanced degrees are strongly preferred (Master’s or PhD)
• 1st Pass LLM Evaluation (60 min est)
• 2nd Professional or Expert level proficiency (C1/C2) in English per valuation assessment 30 min est)

Salary Range: $50,000 – $59,000 USD

Salary rates at Innodata vary depending on a wide array of factors, which may include but are not limited to the role, skill set, educational background and geographic location.