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Rag Internship Jobs (NOW HIRING)

Interon IT Solutions

GenAI Intern

Chantilly, VA · On-site

Chantilly, Virginia -- Onsite Experience: 1-5 years; internship and entry-level candidates ... Develop RAG solutions using enterprise documents and structured or unstructured data. * Build AI ...

UNION HOME MORTGAGE

AI Engineer Intern

Strongsville, OH · On-site

D Internship Program's goal is to provide a fun, interesting, and real-world environment for our ... Build & Improve AI Workflows - Assist in developing LLM, RAG, agentic, and traditional machine ...

GreenSand AI

AI Software Engineering/Research Internship (unpaid) Location: Remote (U.S. based preferred) Type ... LLMs, Agents, RAG, MCP, and Prompt Engineering). * Exceptional core CS skills related to data ...

GreenSand AI

AI Software Engineering/Research Internship (unpaid) Location: Remote (U.S. based preferred) Type ... LLMs, Agents, RAG, MCP, and Prompt Engineering). * Exceptional core CS skills related to data ...

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Rag Internship information

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How much do rag internship jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for rag internship in the United States is $17.31, according to ZipRecruiter salary data. Most workers in this role earn between $14.42 and $19.23 per hour, depending on experience, location, and employer.

What is a RAG internship?

A Rag Internship typically refers to a student internship position that is part of a university's 'Rag' (Raise and Give) society or committee, which organizes fundraising events and campaigns for charitable causes. Interns in these roles assist with event planning, marketing, volunteer coordination, and general administrative tasks to support fundraising initiatives. This position offers students practical experience in event management, teamwork, and communication, while contributing to charitable work. Rag Internships are usually unpaid and are a great way for students to develop transferable skills.

What can I expect from the typical workflow and team collaboration during a RAG internship?

During a RAG internship, you can expect to work closely with machine learning engineers, data scientists, and software developers to design, implement, and optimize information retrieval systems that power generative AI models. Your daily tasks may include processing large datasets, experimenting with retrieval algorithms, evaluating model outputs, and contributing to documentation or presentations. Interns often participate in regular team meetings, code reviews, and brainstorming sessions to discuss technical challenges and share progress. This collaborative environment helps you develop both technical and communication skills, while gaining hands-on experience with state-of-the-art NLP technologies.

What are the key skills and qualifications needed to thrive as a RAG intern, and why are they important?

To excel as a RAG Internship candidate, you should possess a solid understanding of natural language processing, machine learning fundamentals, and programming skills, typically supported by coursework in computer science or data science. Familiarity with tools such as Python, PyTorch or TensorFlow, and experience working with large language models and retrieval systems are often required. Strong problem-solving abilities, attention to detail, and effective communication set outstanding interns apart. These competencies enable interns to contribute meaningfully to AI research teams, support innovative projects, and adapt to the rapidly evolving field of AI.

What is the difference between Rag Internship vs Data Analyst Internship?

AspectRag InternshipData Analyst Internship
Required CredentialsBasic coursework, some technical skillsRelevant degree, proficiency in data tools
Work EnvironmentEntry-level, project-based, team settingsOffice or remote, analytical tasks
Industry UsageCommon in creative and design fieldsWidely used across finance, marketing, tech

Rag Internships typically focus on introductory tasks in creative or design fields, requiring basic skills and offering hands-on experience. Data Analyst Internships are more specialized, demanding relevant technical skills and data knowledge. Both provide valuable industry exposure but differ in skill requirements and work focus.

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Rag Internship jobs near you
Infographic showing various Rag Internship job openings in the United States as of September 2026, with employment types broken down into 14% Internship, 1% As Needed, 68% Full Time, 16% Part Time, and 1% Contract. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $35,995 per year, or $17.3 per hour.

AI Applications Engineering Internship

Houston, TX • On-site

Fervo Energy Company
Clean Energy Semiconductors Manufacturing • 11 - 50 employees
Learn more about Fervo Energy Companyexternal

$16 - $20.75/hr

Internship

Posted 11 days ago

Job description

Job Type
Internship
Description
Internship Overview
You won't be running coffee orders or shuffling paperwork this summer. At Fervo, interns are handed something real: a project of your own, scoped with your manager on day one and yours to drive for the full 12 weeks. You'll work side-by-side with the teams building the next generation of geothermal energy, tackling problems that genuinely move the business forward. At the end of the summer, you'll present your work to our executive leadership team, department leads, and fellow interns, sharing real results with a real audience. This is a real seat at the table - and a real shot at what comes next.
Position Description
Fervo Energy is developing next-generation geothermal power to deliver firm, carbon-free energy at scale, anchored by our flagship Cape Station project in Milford, Utah. We're building a dedicated AI team to unlock transformational value across drilling, reservoir modeling, operations, and commercial strategy, and we're looking for a graduate-level AI Applications Engineering Intern (PhD candidates strongly preferred) to help lead the way.
You'll apply cutting-edge AI to real problems in geothermal development, from hybrid AI-physics models for subsurface forecasting to RAG systems for knowledge management and predictive maintenance models, serving as an internal consultant on Fervo's Strategy Team and partnering with end-user departments to guide decision-makers through complex technical, operational, and commercial challenges
Requirements
Responsibilities
  • Develop, train, and evaluate advanced AI models (LLMs, ML, time-series, hybrid physics-informed)
  • Collaborate with end-user teams to scope and deliver applied AI solutions
  • Contribute to Fervo's centralized AI infrastructure and data architecture
  • Document methodologies and provide clear technical communication to technical and non-technical stakeholders
  • Present findings and recommendations to cross-functional teams, including senior leadership

Required Qualifications
  • Graduate student or PhD candidate in Computer Science, Applied Mathematics, or a related quantitative field with a focus on AI/ML
  • Strong proficiency in Python and machine learning frameworks (e.g., PyTorch, TensorFlow, Scikit-learn)
  • Demonstrated research experience in one or more of: large language models, time-series analysis, physics-informed ML, optimization, or reinforcement learning
  • Ability to apply theoretical knowledge to practical, messy, real-world datasets
  • Excellent problem-solving, communication and collaboration skills
  • Self-starter with the ability to scope and drive projects independently

Preferred Qualifications
  • Experience with energy systems, industrial operations, or geoscience applications
  • Prior experience with RAG architectures, data engineering, or scalable model deployment

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