Applied AI Associate

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:

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

We’re on the lookout for smart, savvy, and curious Generative AI Specialist to join our global contributor community as part of our Subject Matter Expert (SME) on Demand program.

This is not a traditional full-time role.

It’s a part-time, remote, flexible, project-specific opportunity designed for those who want to make a real impact—on their schedule.

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 while maintaining control over your time.

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 gig—it’s a rare chance to help shape the future of AI from anywhere in the world, on your own terms.

• Rating/assessing the performance of AI models or algorithms based on their output or behavior through a set of evaluative questions.
• Labeling elements of a piece of content rather than the content as a whole.
• Assigning predefined categories or labels to items.
• Evaluating the perceived quality and/or appropriateness of content
• Generating labels to advance understanding of a concept, trend etc.
• 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.