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Intern Llm Developer Jobs in Colorado (NOW HIRING)

Intern Llm Developer information

What are the key skills and qualifications needed to thrive as an Intern LLM Developer, and why are they important?

To thrive as an Intern LLM Developer, you generally need a solid background in computer science, programming (especially Python), and foundational knowledge of machine learning concepts. Familiarity with deep learning frameworks like TensorFlow or PyTorch, version control systems such as Git, and exposure to large language models (LLMs) are typically required. Strong problem-solving abilities, attention to detail, and effective communication help you collaborate and learn in dynamic team environments. These skills and qualities are vital for contributing to cutting-edge AI projects and rapidly adapting to evolving technologies in the field.

What does an Intern LLM Developer do?

An Intern LLM (Large Language Model) Developer supports the development, testing, and deployment of AI models, specifically large language models like GPT or BERT. Their responsibilities often include data preprocessing, model fine-tuning, writing code to interact with APIs, and evaluating model performance. Interns work under the guidance of senior developers and researchers to gain hands-on experience in natural language processing and AI. This role is ideal for students or recent graduates looking to build practical skills in machine learning and AI development.

What types of projects and learning opportunities can an Intern LLM Developer expect to work on during their internship?

As an Intern LLM Developer, you can expect to participate in hands-on projects involving the development, fine-tuning, or evaluation of large language models (LLMs). Typical responsibilities include data preprocessing, implementing model training pipelines, and collaborating with senior engineers and data scientists to optimize model performance. You'll also have opportunities to contribute to research on natural language processing (NLP) tasks and gain exposure to industry-standard tools and frameworks. This role offers valuable mentorship and the chance to build practical skills in machine learning and AI, setting a strong foundation for a future career in the field.

What is the difference between Intern Llm Developer vs Intern Machine Learning Engineer?

AspectIntern Llm DeveloperIntern Machine Learning Engineer
Required CredentialsTypically pursuing or recent graduate in Computer Science, AI, or related fieldsSimilar educational background, often with focus on ML or AI
Work EnvironmentTech companies, AI startups, research labsTech firms, startups, research institutions
Employer & Industry UsageFocused on developing large language models and NLP applicationsDeveloping various ML models, including NLP, computer vision, etc.
Common Search & ComparisonIntern Llm Developer vs Intern Machine Learning Engineer

Intern Llm Developers primarily focus on building and fine-tuning large language models, often specializing in NLP tasks. Intern Machine Learning Engineers have a broader scope, working on various ML models across different domains. Both roles require similar educational backgrounds and are found in tech and AI industries, but their specific focus areas differ.

What are popular job titles related to Intern Llm Developer jobs in Colorado? For Intern Llm Developer jobs in Colorado, the most frequently searched job titles are:
Infographic showing various Intern Llm Developer job openings in Colorado as of July 2026, with employment types broken down into 81% Full Time, 6% Part Time, 1% Temporary, and 12% Contract. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution.
Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants

Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants

The National Renewable Energy Laboratory (NREL)

Golden, CO • On-site

Full-time

Medical, Dental, Vision, Retirement

Posted 6 days ago


Job description

Posting Title
Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants
Location
CO - Golden
Position Type
Intern (Fixed Term)
Hours Per Week
40
Working at NLR
NLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job Description
The AI, Learning and Intelligent Systems group in the NLR Computational Science Center has an opening for a graduate student researcher in LLM Reliability and Uncertainty for AI Science Assistants. The researcher will investigate methods for quantifying uncertainty in LLM-based science assistants over multi-turn scientific dialogue, with an emphasis on flagging when a scientific question or task is underspecified or ill-posed. In practice, scientific questions can be vague, open-ended, or underdetermined. LLM-based assistants can quietly insert their own assumptions into such requests to fill the gap instead of raising concerns to their human counterpart. This internship will investigate if the assistant's internal representations can be probed to detect these instances so they may be flagged for the user or used to trigger clarifying questions. We are looking for a dynamic, motivated researcher with a strong technical background and an interest in AI for science, uncertainty-aware machine learning, human-AI scientific workflows, and trustworthy AI. The successful candidate must be able to work at the intersection of machine learning research and practical AI system integration.
Responsibilities include:
  • Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows
  • Develop probing methods that predict, from a model's internal representations, when a scientific task specification is incomplete or inconsistent and a clarifying question is warranted
  • Build and instrument evaluation pipelines that capture and analyze model internal states over multi-turn scientific dialogue on HPC systems
  • Conduct experiments and analyze model behavior across computational science domains and established benchmarks
  • Contribute to technical documentation, research reports, publications, and presentations summarizing project progress and findings
  • Develop, test, and maintain high-quality research code and evaluation pipelines

Basic Qualifications
Minimum of a 3.0 cumulative grade point average.
Undergraduate: Must be enrolled as a full-time student in a bachelor's degree program from an accredited institution.
Post Undergraduate: Earned a bachelor's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate: Must be enrolled as a full-time student in a master's degree program from an accredited institution.
Post Graduate: Earned a master's degree within the past 12 months. Eligible for an internship period of up to one year.
Graduate + PhD: Completed master's degree and enrolled as PhD student from an accredited institution.
Please Note:
• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
* Must meet educational requirements prior to employment start date.
Additional Required Qualifications
  • Familiarity with large language models, including agentic, tool-using, or multi-turn conversational LLM systems
  • Experience developing or evaluating machine learning models for classification, uncertainty estimation, or related tasks
  • Knowledge of probabilistic machine learning or uncertainty quantification concepts
  • Hands-on experience with open-weight LLMs and modern deep learning frameworks
  • Experience running Python code on HPC or multi-GPU systems
  • Strong software engineering and debugging skills
  • Ability to work independently while collaborating effectively in a multidisciplinary research environment

Preferred Qualifications
  • Research experience related to hallucination detection, uncertainty quantification, interpretability, explainability, or trustworthy AI
  • Familiarity with representation probing or mechanistic interpretability methods
  • Experience with LLM benchmarking and evaluation, including multi-turn or conversational agent evaluation and LLM-as-a-judge protocols
  • Experience with scientific question-answering systems, AI for science applications, or scientific agent frameworks
  • Coursework or research background in a computational science domain (e.g., fluid mechanics, solid mechanics, materials science, or numerical methods for PDEs)

Job Application Submission Window
The anticipated closing window for application submission is up to 30 days and may be extended as needed.
Annual Salary Range (based on full-time 40 hours per week)
Job Profile: / Annual Salary Range: $44,500 - $71,200
NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits Summary
Benefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
* Based on eligibility rules
Badging Requirement
NLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.
Drug Free Workplace
NLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission Guidelines
Please note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
Reasonable Accommodations
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