ZipRecruiter

Log In

2

Part Time Llm Training Jobs (NOW HIRING)

Pika

Research Intern (BS/MS/PhD)

Palo Alto, CA · On-site

In this role, you will directly contribute to mid-training and post-training efforts for Videogen ... Flexible part-time and full-time internship terms are available. What You'll Do * Work directly on ...

next page

Showing results 1-20

All Jobspart time JobsPart Time Llm Training Jobs

Part Time Llm Training information

See salary details

$32K

$68.7K

$112K

How much do part time llm training jobs pay per year?

As of Aug 29, 2026, the average yearly pay for part time llm training in the United States is $68,682.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $84,500.00 per year, depending on experience, location, and employer.

What is a part time LLM training?

A part time LLM training job typically involves working with organizations or companies to help train large language models (LLMs) such as ChatGPT or similar AI systems. These roles may include tasks like data annotation, prompt engineering, content evaluation, or providing feedback on AI outputs. Part time LLM trainers usually have flexible schedules and contribute remotely, making it a popular choice for students, professionals, or individuals seeking supplementary income. The position often requires strong language skills, attention to detail, and sometimes subject matter expertise depending on the focus of the LLM being trained.

What are the typical responsibilities and expectations for someone in a part-time LLM training role?

In a part-time LLM training role, your primary responsibility is to assist in training large language models by generating, reviewing, or annotating text data according to specific guidelines. You may also be asked to evaluate AI outputs for quality and relevance or provide feedback to improve model performance. The work is often remote and independent, but you will typically collaborate with project managers and fellow annotators through digital platforms. Attention to detail and consistency are crucial, as your input directly influences the model's learning and accuracy. Flexibility in hours provides a good balance for those seeking supplemental income or experience in the AI field.

What are the key skills and qualifications needed to thrive as a part-time LLM training specialist, and why are they important?

To thrive as a Part-Time LLM Training Specialist, you need a strong background in machine learning, natural language processing, and data annotation, often supported by relevant degrees or experience in AI or computer science. Familiarity with tools such as Python, annotation platforms, and version control systems is typically required. Attention to detail, critical thinking, and effective communication are crucial soft skills for accurately labeling data and collaborating with engineering teams. These skills ensure the quality and efficiency of model training processes, ultimately improving AI system performance.

What is the difference between Part Time Llm Training vs Paralegal?

AspectPart Time Llm TrainingParalegal
Required CredentialsLaw degree or equivalent, often pursuing specializationAssociate's degree or paralegal certificate
Work EnvironmentAcademic settings, law firms, or online programsLaw firms, corporate legal departments, government agencies
Industry UsageLegal education, specialization, academic advancementLegal support, case preparation, client communication

Part Time Llm Training focuses on advanced legal education for specialization or academic purposes, often pursued alongside work. Paralegals assist lawyers with casework and legal support tasks. While both roles operate within the legal industry, Llm students are primarily engaged in learning, whereas paralegals are involved in practical legal support work.

What are the most commonly searched types of Llm Training jobs?

The most popular types of Llm Training jobs are:

Part Time Llm Training jobs near you

AI/ML Engineer - LLM & AI Harness Engineering - 100% Remote US

WilsonCTS

Middletown, PA • On-site, Remote

$125/hr

Full-time, Part-time, Contractor

Posted 13 days ago

Job description

AI/ML Engineer - LLM & AI Harness Engineering

Location: 100% Remote - United States
Schedule: Monday-Friday, 8:00 AM-5:00 PM ET
Duration: 12-Month Contract
Compensation: Up to $125/hour
Hours: Full-time preferred; part-time may be considered for the right candidate

About the Opportunity

A leading global technology and engineering company is seeking an experienced AI/ML Engineer to join its Digital Data Networks organization and help build practical AI solutions that accelerate engineering productivity, technical data analysis, and decision-making.

This is a highly hands-on role focused on AI/LLM harness engineering. You will build Python-based solutions around existing AI models, incorporating LLMs, Retrieval-Augmented Generation (RAG), AI agents, tool calling, structured workflows, evaluation, and guardrails.

The ideal candidate combines strong AI/ML engineering skills with the ability to understand and work with complex technical and engineering data.

What You'll Do

  • Develop and validate Python-based AI/ML and LLM workflows for engineering analysis, technical data processing, automation, and decision support.

  • Build model training and validation pipelines using open datasets and adapt approaches for engineering datasets such as s-parameters, VNA, simulation, test, and other measurement data.

  • Apply machine learning and deep learning techniques, including neural networks, CNNs, and LSTM/recurrent models, to practical engineering challenges.

  • Develop LLM workflows for data parsing, summarization, extraction, classification, and structured outputs using local or hosted AI models.

  • Design and implement RAG solutions that ground AI responses in trusted engineering documents, datasets, and approved knowledge sources.

  • Build AI-agent and LLM harness workflows incorporating task routing, tool calling, workflow orchestration, evaluation, and guardrails.

  • Develop or integrate custom tools that allow AI workflows to interact with engineering and technical data sources.

  • Collaborate with signal integrity, product development, testing, manufacturing, and operations teams to identify opportunities for AI automation and decision support.

  • Translate technical requirements into reliable, reusable AI workflows and prototypes.

  • Document AI workflows, assumptions, validation approaches, limitations, and recommended next steps.

  • Evaluate AI-generated results, identify limitations or risks, and make data-driven recommendations for improvement.

Required Qualifications

  • Bachelor's degree in Engineering, Computer Science, Data Science, Applied Mathematics, or a related technical discipline. Master's degree is a plus.

  • Strong hands-on experience with Python for AI/ML development, data processing, model training, validation, and automation.

  • Solid understanding of machine learning and deep learning, including neural networks, CNNs, and LSTM/recurrent architectures.

  • Experience with AI/ML frameworks such as PyTorch, TensorFlow, or equivalent.

  • Understanding of GPU-enabled AI/ML development and CUDA, particularly in NVIDIA environments.

  • Practical knowledge of Large Language Models (LLMs) and experience working with open-source and/or commercial AI models.

  • Experience with local LLM environments or model-serving tools such as Ollama, LM Studio, llama.cpp, or equivalent.

  • Experience with Hugging Face, LangChain, or similar AI/LLM frameworks.

  • Strong understanding of Retrieval-Augmented Generation (RAG) and experience implementing RAG-based workflows.

  • Ability to design AI-agent/harness architectures incorporating RAG, tool calling, workflow orchestration, evaluation, guardrails, and external data sources.

  • Strong analytical and problem-solving abilities with a focus on validating AI outputs and understanding model limitations.

  • Excellent communication skills and the ability to explain AI concepts and technical tradeoffs to engineering stakeholders.

  • Ability to work independently, learn quickly, and collaborate effectively within a global technical organization.

Nice-to-Have Experience

  • Experience applying AI/ML or LLMs to engineering, signal-integrity, measurement, simulation, test, or product-development datasets.

  • Experience developing custom AI tools for engineering measurement, simulation, test, or product-development workflows.

  • Experience using Generative AI to support product design, engineering parameter optimization, or design iteration.

  • Experience using AI to identify product defects, performance issues, root causes, and corrective actions.

  • Experience with AWS-based AI/data environments, including databases, queues, notebooks, or related infrastructure.

  • Strong experience with Python/Jupyter notebooks for rapid prototyping and technical demonstrations.

  • Experience evaluating user or engineering performance with and without AI assistance.

  • Understanding of GPU resource planning and compute constraints impacting AI/ML development.

  • Hands-on experience with LLM fine-tuning, domain-specific model adaptation, training-data development, model serving, or GPU optimization.

  • Experience working in high-speed interconnect, cable assembly, signal integrity, or related engineering/product-development environments.

Why This Role?

This is an opportunity to work at the intersection of AI, LLMs, software engineering, and advanced engineering applications. You'll have the opportunity to move beyond experimentation and build practical AI systems that can be used by technical teams to analyze data, automate workflows, improve engineering decisions, and accelerate product development.

Apply