Graduate (Year-Round) Intern – Natural Language Processing & Large Language Models

Posting Title

Graduate (Year-Round) Intern – Natural Language Processing & Large Language Models

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Location

CO - Golden

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Position Type

Intern (Fixed Term)

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Hours Per Week

40

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Working at NREL

The National Renewable Energy Laboratory (NREL), located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for research and development of renewable energy and energy efficiency technologies.

Join a team of world-class scientists, engineers, and visionaries dedicated to shaping the world’s energy future through cutting-edge research and innovation. From our vision to our NREL community, we are unique in the research community. We are focused on impact. From our work in basic sciences to systems engineering, analysis, demonstration, and deployment, we are focused on solving market-relevant problems that result in advanced, secure, reliable, and affordable energy systems. We are trusted clean energy leaders, developing cost-saving solutions that make U.S. industries more competitive, and support job creation and economic growth across rural and urban communities.

At NREL, we offer a unique, mission-driven work environment with cutting-edge facilities and multidisciplinary research teams. NREL's environment offers strong partnerships with industry, academia, and other national laboratories, as well as professional development opportunities and a competitive benefits package for employees.

Learn about NREL’s critical objectives: NREL's Mission and Vision.

Job Description

The AI, Learning, and Intelligent Systems (ALIS) Group in the NREL Computational Science Center has an opening for a graduate research intern in the areas of natural language processing and large language models (LMs) . This intern will work with a mentor on evaluating and improving the ability of LMs to answer probabilistic questions about hypothetical situations posed by scientists. A harmonious combination of LM’s broad world knowledge with reasoning about the plausibility of scientific ideas, which may only be verifiable through scientific experimentation or simulation, has significant implications for the trustworthiness of LM assistants. This project is part of a collaboration led by NREL investigating fundamental advances for LM-based science assistants. This remote-friendly position includes access to cutting-edge Department of Energy high-performance computing (HPC) systems equipped with the latest hardware accelerators for distributed computing.

Responsibilities:

• Collaborate with your research mentor and other project members to define a scope with the goal of submitting a manuscript at project’s end
• Design, implement, and run experiments using HPC and cloud computing resources
• Communicate your research progress verbally and in writing through regular project updates and in the form of a publication

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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

The successful candidate must have published at least one first-author paper on a topic related to the ALIS group's work.

To be considered, all candidates must include in their resume an online code repository such as GitHub with several publicly visible coding projects. Alternatively, please explain in your cover letter why your application does not include a code repository and/or several examples of previous projects.

Preferred Qualifications

• Ideally enrolled in a Ph.D. program in a relevant field of study
• Demonstrated experience writing maintainable (e.g., well-documented) open-source machine learning research code
• Demonstrated experience using distributed training frameworks (preferably PyTorch)
• Strong background in applied math, probability, and algorithms
• Genuine interest in AI applications in science & engineering, and NREL’s mission
• Experience with managing software dependencies in high performance computing (HPC) environments with commons tools such as Modules, Conda, and containerization
• General knowledge and application of standards, principles, concepts and techniques in software engineering

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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: $50,200 - $80,300

NREL 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). NREL 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

NREL 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

NREL 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 NREL, 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 NREL 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.

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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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