1

Large Language Models Intern Jobs (NOW HIRING)

We also develop state-of-the-art generative AI technologies based on Large Language Models to power innovative features in both Apple's devices and services on the cloud. As part of this group, you ...

next page

Showing results 1-20

Large Language Models Intern information

See salary details

$25.5K

$42.6K

$88K

How much do large language models intern jobs pay per year?

As of Jun 7, 2026, the average yearly pay for large language models intern in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Large Language Models Intern, and why are they important?

To thrive as a Large Language Models Intern, you need a strong background in computer science, mathematics, and machine learning, typically supported by coursework or experience in natural language processing (NLP). Familiarity with programming languages like Python, deep learning frameworks such as TensorFlow or PyTorch, and version control systems like Git is essential. Curiosity, problem-solving ability, and effective communication are important soft skills that help you collaborate and innovate in research-driven environments. These skills and qualities are vital for contributing to cutting-edge AI projects and adapting to the rapidly evolving field of large language models.

What does a Large Language Models Intern do?

A Large Language Models Intern assists in the research, development, and evaluation of advanced AI models that process and generate human language, such as GPT or BERT. Their work may include data preprocessing, designing and testing model architectures, fine-tuning existing models, and analyzing results. Interns often collaborate with machine learning engineers and researchers to contribute to ongoing projects, learning about the latest advancements in natural language processing (NLP). They may also help in writing code, preparing reports, and presenting findings. This role is ideal for those interested in AI, computational linguistics, or software engineering.

What types of projects can a Large Language Models Intern expect to work on during their internship?

As a Large Language Models Intern, you can expect to work on a variety of projects such as data preprocessing, model fine-tuning, performance evaluation, and the development of new features for natural language processing applications. Interns often collaborate closely with research scientists and machine learning engineers to analyze model outputs, experiment with novel architectures, and contribute to the improvement of existing language models. This role provides an excellent opportunity to gain hands-on experience with state-of-the-art NLP technologies, and to make meaningful contributions to ongoing research or product development.
Infographic showing various Large Language Models Intern job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 94% Full Time, 3% Part Time, 1% Temporary, and 1% Contract. Highlights an 95% Physical, 2% Hybrid, and 3% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Large Language Model (LLM) AI Engineer

Large Language Model (LLM) AI Engineer

Oran, Inc.

Herndon, VA โ€ข On-site

Full-time

Posted 17 days ago


Job description

Experience Required
7+ Years Overall | 3+ Years in Generative AI / LLMs
Position Overview
We are seeking a Large Language Model (LLM) AI Engineer to design, fine-tune, evaluate, and integrate generative AI and LLM-based solutions in healthcare, scientific, and regulated environments. The ideal candidate will possess expertise in modern AI architectures, vector databases, prompt engineering, and AI governance.
Key Responsibilities
  • Design and implement generative AI and LLM solutions.
  • Fine-tune and evaluate foundation models.
  • Develop AI workflows using agentic AI frameworks.
  • Build RAG architectures and vector database integrations.
  • Develop APIs and cloud-native AI solutions.
  • Implement hallucination mitigation and AI governance controls.