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Part Time Large Language Model Llm Jobs (NOW HIRING)

... large language models (LLMs). We're on the lookout for smart, savvy, and curious Generative AI ... It's a part-time, remote, flexible, project-specific opportunity designed for those who want to ...

... large language models (LLMs). We're on the lookout for smart, savvy, and curious Generative AI ... It's a part-time, remote, flexible, project-specific opportunity designed for those who want to ...

... large language models (LLMs). We're on the lookout for smart, savvy, and curious Generative AI ... It's a part-time, remote, flexible, project-specific opportunity designed for those who want to ...

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Part Time Large Language Model Llm information

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$38

How much do part time large language model llm jobs pay per hour?

As of Sep 14, 2026, the average hourly pay for part time large language model llm in the United States is $24.34, according to ZipRecruiter salary data. Most workers in this role earn between $18.99 and $29.09 per hour, depending on experience, location, and employer.

What is a part time large language model LLM?

A Part Time Large Language Model (LLM) role typically involves working with large language models, such as GPT or similar AI systems, on a part-time basis. Responsibilities may include training, fine-tuning, evaluating, or deploying LLMs to solve specific problems or improve existing language technologies. These roles often require knowledge of natural language processing, machine learning, and programming. Part-time positions allow for flexible working hours and may be suitable for students, researchers, or professionals looking to contribute to AI projects without a full-time commitment.

What skills and qualifications are needed to thrive as a part time large language model LLM?

To thrive as a Part-Time Large Language Model (LLM) Engineer, you need a strong background in computer science, machine learning, and natural language processing, often supported by a relevant degree or equivalent experience. Proficiency with tools and frameworks like Python, TensorFlow, PyTorch, and experience working with LLM architectures such as GPT or BERT is typically required. Strong analytical thinking, communication skills, and the ability to collaborate effectively in a remote or distributed environment are valuable soft skills. These competencies are crucial for successfully developing, fine-tuning, and deploying advanced language models that meet organizational goals.

What are common challenges faced when working part time with large language models LLMs?

Part-time professionals working with Large Language Models often face challenges in keeping up with rapid advancements in AI technologies and adapting to evolving best practices. Balancing limited work hours with the need to stay updated on new research, tools, and model updates can be demanding. Additionally, coordinating with full-time team members and integrating contributions within tight timelines requires effective communication and collaboration skills. However, the flexible schedule can also provide opportunities for continuous learning and professional growth.

What is the difference between Part Time Large Language Model Llm vs Part Time Data Annotator?

AspectPart Time Large Language Model LlmPart Time Data Annotator
Required CredentialsKnowledge of NLP, AI, and machine learning conceptsAttention to detail, basic understanding of data labeling
Work EnvironmentRemote or office-based, involving AI development teamsRemote or on-site, working with datasets and labeling tools
Employer & Industry UsageTech companies, AI research labs, startupsData companies, AI firms, machine learning projects

While both roles support AI development, a Part Time Large Language Model Llm focuses on understanding and improving language models, requiring technical knowledge. A Part Time Data Annotator primarily labels data to train models, emphasizing accuracy and attention to detail. The roles differ in technical complexity but are both essential in AI workflows.

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Infographic showing various Part Time Large Language Model Llm job openings in the United States as of September 2026, with employment types broken down into 1% As Needed, 83% Full Time, 14% Part Time, and 2% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $50,625 per year, or $24.3 per hour.

Generative AI Associate - Flexible Hours

Remote

Innodata Inc.
IT ServicesΒ β€’Β 501 - 1,000 employees

$15/hr

Full-time, Part-time

Re-posted 20 days ago


Innodata rating

7.5

Company rating: 7.5 out of 10

Based on 6 frontline employees who took The Breakroom Quiz


Job description

Scope of the Role:Β 

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.Β 

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.Β 

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.Β 

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.Β Β 
  • Labeling elements of a piece of content rather than the content as a whole.Β Β 
  • Assigning predefined categories or labels to items.Β Β 
  • Evaluating the perceived quality and/or appropriateness of contentΒ Β 
  • Generating labels to advance understanding of a concept, trend etc.Β Β 
  • 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.Β Β 
  • Reviewing data and identifying whether or not a product feature works as intended based on the project's guidelines.Β Β 
  • Labeling model outputs to identify if a piece of content is or isn't something. Examples: identify clickbait; identifying gaming videos; identifying branded content.Β 
  • Ordering or ranking items based on a set of preferences or criteria.Β Β 
  • Creating prompts or questions that will be used to generate responses from a language model or other AI system.Β Β 
  • Projects that evaluate the relevance of content based on a relevancy scale (1-3, 1-5, etc.).Β Β 
  • Generating responses to prompts or questions using a language model or other AI system.Β Β 
  • Rewriting existing text while preserving the original meaning, often to improve clarity or style and adherence to guidelines.Β Β 
  • Producing concise summaries of longer pieces of text or data.Β Β 
  • Converting spoken language or audio content into written text.Β Β 
  • Converting text or spoken language from one language to another.Β Β 
  • 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.

You'll Thrive in This Role If You Have:

  • A Bachelor's degree or higher in aΒ humanities specializationΒ is required. Advanced degrees are strongly preferred (Master's or PhD)
  • Professional or Expert level proficiency (C1/C2) in EnglishΒ 

The expected hourly salary range for this position is $15 p/hour, based on experience, skills, and qualifications.


What Innodata employees say

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