Generative AI Associate(PHD) - Flexible Hours

Innodata; United States; Hybrid; USD 34,440 - 68,880; Part time

Benefits offered by this job

Remote work

Flexible hours

Part-time project-based

Job summary

Innodata seeks a Generative AI Associate (PhD) for a flexible, remote, part-time engagement in the United States. The role focuses on evaluating AI models, labeling data, and creating training data to advance AI development while fitting the candidate’s schedule.

The position supports a global SME-on-Demand program, enabling contributions to cutting-edge AI projects from anywhere in the country with flexible hours.

Qualifications

• Experience labeling or annotating data for AI systems is preferred.
• Ability to follow guidelines and ensure data quality.
• Strong analytical and reasoning skills.
• Familiarity with large language models is a plus.
• Independent work style with attention to detail.

Responsibilities

• Rate and assess AI model outputs based on defined guidelines.
• Label content segments for training and evaluation.
• Assign predefined categories or labels to data items.
• Evaluate content quality and suitability against project rules.
• Generate labels to advance model understanding and performance.
• Create additional training data through transformations (text paraphrasing, summarization).
• Produce concise training data through editing, rewriting, or summarization.
• Review data to confirm feature behavior aligns with project specs.
• Identify content that is clickbait, branded, or inappropriate.
• Rank items according to defined criteria.
• Create prompts or questions to elicit model responses.
• Evaluate content relevance on a given scale (1–5).
• Generate model prompts and responses for development tasks.
• Rewrite existing text while preserving meaning and style.

Skills

Data labeling

Content evaluation

Prompt engineering

AI model evaluation

Research mindset

Education

PhD in AI/ML or related field

Job description

Generative AI Associate(PHD) - Flexible Hours

Remote - United States

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.

What You’ll Own:

• 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.
• Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.
• Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content.
• Ordering or ranking items based on a set of preferences or criteria.
• Creating prompts or questions that will be used to generate responses from a language model or other AI system.
• Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).
• Generating responses to prompts or questions using a language model or other AI system.
• Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.
• Producing concise summaries of longer pieces of text or data