You are viewing a preview of this job. Log in or register to view more details about this job.

NSIP Undergraduate Technical Intern - AI & Data Analytics Division

Pacific Northwest National Laboratory
Email

Overview

Protecting U.S. residents and visitors is among our nation’s highest priorities. As adversaries gain access to sophisticated technologies and materials, the threats grow more dynamic and complex—from cyber and nuclear to chemical and biological weapons of mass effect and other forms of terrorism. The PNNL national security mission employs our researchers, tools, and technologies to play a key role in advancing the ability to identify and secure nuclear materials, detect weapons of mass effect, manage nonproliferation treaties, secure our nation's borders, and protect critical infrastructures.  PNNL’s scientific discovery and capabilities—rooted in innovative theory, methods, algorithms, and tools—are enabling stronger, more resilient technologies and systems to safeguard national security. Coupled with decades of radiological and nuclear materials expertise, advanced computing and threat analysis capabilities, and a broad fundamental science base, we are identifying and countering emerging threats that have significant impact at home and around the globe.

To learn more:

http://workbasedlearning.pnnl.gov/default.aspx?topic=National_Security_Internship_Program_(NSIP)

http://www.pnnl.gov/nationalsecurity/technical/capabilities/computing/

https://www.youtube.com/watch?v=Mz2UPYcl_uI

Note: Several documents are strictly required to submit with the application. Please read the entire job posting for detailed instructions.


Responsibilities

PNNL is seeking undergraduate students for assignments within the National Security Internship Program (NSIP). The AI and Data Analytics Division is looking for individuals who have a passion for solving critical national challenges using advanced computational, statistical, and mathematical techniques. The intern will be given an opportunity to be presented with complex problems in national security, energy, and science; apply cutting-edge research to make our nation safer and stronger; develop complex computer code; develop and participate in cyber competitions; design new visualization; work with big data and optimize solutions in diverse domains.

Positions are available in Richland, WA and Seattle, and Sequim WA, based on business need. Positions may also be performed remotely or on campus at PNNL based on business need.

Participants will be starting in cohort sessions and must be available to start in May or June 2025 [Summer cohort].

Diverse Focus Areas: Your internship can be in one of five technical groups.

  • Math, Stats, and Data Science: We employ powerful tools and techniques, such as mathematical modeling and computational statistics, graph and game theory, network science, and uncertainty quantification to solve complex problems in a variety of domains.
    Disciplines: Applied Mathematics, Machine Learning, Statistics, Operations Research
     
  • Applied AI Systems: We develop hardened and robust models to distill large, fast, distributed, and messy data into knowledge to support decision processes in operational environments on sponsor systems.
    Disciplines: Artificial Intelligence, Applied Machine Learning, Data Science, Deep Learning, Computer Vision, Geospatial Intelligence, and Natural Language Processing
     
  • Foundational Data Science: We conceptualize and develop fundamentally new algorithms and tools to address unresolved challenges in distilling large, fast, distributed, and messy data into knowledge to support sponsors’ decision processes.
    Disciplines: Artificial Intelligence, Applied Machine Learning, Data Science, Deep Learning, Computer Vision, Geospatial Intelligence, and Natural Language Processing
     
  • Software Engineering & Architectures: We develop high-quality, scalable, cloud-first solutions for tackling large data pipelines and analytics that are delivered to operational sponsor environments. We use industry best practices for professional software development using Agile development practices, code reviews, automated testing, and CI/CD pipelines.
    Disciplines: Cloud Engineering, Large-Scale Data Engineering, Scalable Machine Learning/Artificial Intelligence, DevSecOps, Automated Testing, Software Engineering
     
  • Human Centered Computing: We combine innovative interactive visualizations with advanced automated data analysis techniques to enable users to gain deeper insights from their data. Make complex data useful through skillful visual design, compelling human computer interaction, sound analytic methods, and solid engineering.
    Disciplines: Data Science, Human-Computer Interaction, User Experience, Software Engineering, Natural Language Processing, Graph Analytics and Data Visualization

 

****HOW TO APPLY****

To have a complete application package, the (2) listed items below are required and must be uploaded correctly per the steps below for consideration:

Step 1: Upload Resume or CV in “Resume” section only (note: If applying to multiple positions, the most recent resume uploaded will be used for all positions an applicant applies to).

Step 2: Upload a Cover Letter separately in “Additional Documents” section of the application titled “AIDA - Summer 2025 NSIP Cover Letter”.

  • Cover letter should include: Statement of interest, reference relevant college courses, and relevant experience that may include extracurricular activities that have prepared you for this career.

If any of the components (resume and cover letter) are not uploaded per the instructions above, applications will be rejected and will no longer be considered. Electronic applications will be accepted until 5:00 p.m. (PST) on the posting close date.


Qualifications
Minimum Qualifications:

  • Candidates must have a high school diploma /GED or higher
  • Candidates must be degree-seeking undergraduate students enrolled at an accredited college or university
  • Candidates must be taking at least 6 credit hours and have an overall cumulative GPA of 3.30