Data Science Institute Undergraduate Student Intern - Summer 2026

Company Description

Join us and make YOUR mark on the World! 

Are you interested in joining some of the brightest talent in the world to strengthen the United States’ security? Come join Lawrence Livermore National Laboratory (LLNL) where our employees apply their expertise to create solutions for BIG ideas that make our world a better place.

We are dedicated to fostering a culture that values individuals, talents, partnerships, ideas, experiences, and different perspectives, recognizing their importance to the continued success of the Laboratory’s mission.

Pay Range

$22.80 - $31.00 Hourly for the 705.1 level

This position is under a step structure.  Please note that the step placement is determined by your most recent completed academic year.

Job Description

Position will be closed to applicants on January 30, 2026.   

Internship Dates: 

Please select the session that best works for you and your mentor’s schedule: 
Session 1: May 18, 2026 - August 7, 2026 

Session 2: June 22, 2026 - September 11, 2026 

We have multiple openings for our Undergraduate summer internship program in data science. Our openings are for undergraduate level students to join the Data Science Summer Institute (DSSI).  The DSSI is a 12-week summer internship program and selected interns will be given the opportunity to engage in practical research to further their educational goals. The selected interns will work in the data science field and provide technical and/or research support to projects in the area of Machine Learning, Statistics, High Performance Computing, and other related fields. If you are graduating with your bachelor’s degree this summer, then apply to the Data Science Summer Institute Graduate Student Intern position.  

Before applying, please understand the following: 

This is an on-site internship; attendance in person is required. Confirm with your university that you are permitted to participate in and work for the full 12-week internship program (i.e. CPT hours). Modifications to the internship dates will not be accepted. If you have pre-planned travel or commitments during the internship, this needs to be disclosed in the interview process. For FAQ, please visit: https://data-science.llnl.gov/dssi/apply 

You will 

• Work with scientists, engineers and technical staff members to provide technical and/or research support to projects in the areas of computational science, numerical methods, mathematics, or other related fields. 

• Participate in research in assigned areas under close supervision. 

• Gather and analyze data and information in support of scientific research. 

• Participate in research planning and evaluation discussion. 

• Work with other DSSI interns on a data science challenge problem. 

• Attend data science related short courses/seminars, meetings, tours, and events. 

• Present work through presentations and/or poster sessions during your internship. 

• Perform other duties as assigned. 

Qualifications

• Must be a continuing college or university student in good standing at an accredited institution pursuing an undergraduate degree in Computer Science, Statistics, Mathematics, Machine Learning, Computer Vision, Bioengineering, or other related fields. 

• Effective programming skills in a high-level language such as R, Python or Matlab. Ability to apply basic principles of data sciences (machine learning, statistics, computer science, mathematics) to solve technical problems. 

• Ability to present and communicate concepts and ideas. 

• Ability to work in a team environment. 

• Effective communication skills. 

• Desire to participate in individual or team efforts including the LLNL DSSI Challenge Problems. 

• Desire to improve skills in public presentation of scientific results by giving presentations. 

• Eagerness to obtain an understanding of new application areas. 

• Exposure through coursework or relevant experience to some of the following topics:  

  • Statistical modeling and data analysis 

  • Bayesian and frequentist statistical frameworks 

  • Inverse problems 

  • Uncertainty quantification 

  • Machine learning 

  • Computer vision 

  • Multimedia signal and video processing 

  • Combinatorics and algorithms 

  • Graph modeling and social network analysis 

  • Modeling and si