What Your Job Will Be Like:
We are seeking a driven and results-oriented graduate student intern for our diverse team of researchers leading Artificial Intelligence (AI) Credibility for Sandia. The selected applicant will engage in project participation, applied research and development, software engineering and deployment, and innovative collaboration.
On any given day, you may be called on to:
Perform as part of a technical team conducting creative AI research.
Collaborate on interdisciplinary teams to research, develop, and deploy AI solutions.
Independently train large language and vision models through continued pretraining, supervised fine tuning, and federated learning.
Develop robust evaluation frameworks for AI models including, but not limited to, large language and vision models.
-
Design and orchestrate new large language model (LLM) training strategies to address language model limitations.
The selected applicant can be a remote worker located in any U.S. State or District of Columbia. Regular or periodic travel to your assigned work location may be required.
Salary Range:
At Sandia, we value the important work done by our interns and its contribution to National Security. Because of this, our interns earn competitive pay rates. Our pay structure is based on earned credit hours, classification, and degree level. Your pay rate will be determined during the hire process and included in your offer package. You can view the Intern Pay Rate chart here ( .
Qualifications We Require:
You bring the confidence and skills to be eligible for the job by meeting these minimum requirements:
Earned bachelor's degree
Currently attending and enrolled full time in an accredited science, engineering, or math graduate program
Minimum cumulative GPA of 3.0/4.0
Ability to work up to 30 hours per week during the academic year, and up to 40 hours per week during the summer
Ability to secure and maintatin a U.S. security clearance which requires U.S. citizenship
Qualifications We Desire:
Excellent interpersonal and communication skills.
Experience acquiring, preparing, and analyzing real world data.
Currently pursuing a master's or doctorate degree in mathematics, computer science, electrical engineering, or a related field.
R&D experience including project work, technical presentations and/or publications.
Familiarity with PyTorch and a strong grasp on standard AI/LLM training libraries (e.g., transformers and vllm).
Research experience involving AI/LLM evaluation and benchmarking.
Experience working with high performance computing systems and Slurm queues.
Ability to obtain and maintain a DOE-Q clearance.
Posting Duration:
This posting will be open for application submissions for a minimum of three (3) calendar days, including the 'posting date'. Sandia reserves the right to extend the posting date at any time.
About Our Team:
The AI Credibility team was created to respond to the national call to develop, deliver, and deploy AI to enable and accelerate science, engineering, and discovery across the Department of Energy¿s high-consequence mission areas. We are developing the team to represent the key capability area of AI Credibility at Sandia, harnessing a multidisciplinary group of leaders across many elements of this critical research area to advance the state of the art. Our team¿s mission is simple, but the research opportunities are vast: we will enable the use of AI in Sandia¿s high-consequence missions through the research, development, and application of credibility process methods for AI.
About Sandia:
reputed company is the nation’s premier science and engineering lab for national security and technology innovation, with teams of specialists focused on cutting-edge work in a broad array of areas. Some of the main reasons we love our jobs:
Challenging work with amazing impact that contributes to security, peace, and freedom worldwide
Extraordinary co-workers
Some of the best tools, equipment, and research facilities in the world
Career advancement and enrichment opportunities
Flexible work arrangements for many positions include 9/80 (work 80 hours every two weeks, with every other Friday off) and 4/10 (work 4 ten-hour days each week) compressed workweeks, part-time work, and telecommuting (a mix of onsite work and working from home)
-
Generous vacation, str
Share
Share
