Year-Round Graduate Internship - AI/Machine Learning Time Series Modeling

Role overview Contribute to research on AI foundation models for electric grid applications, including weather and grid forecasting. This year-round graduate internship focuses on pre-training and fine-tuning models with high-performance computing resources, then communicating findings through scientific materials and presentations.

Responsibilities - Pre-train and fine-tune large AI foundation models using high-performance computing. - Apply machine learning methods to time-series forecasting problems relevant to weather, energy, or the power grid. - Prepare slide decks and reports that explain research methods and results. - Share findings through papers and presentations. - Take on related research duties as assigned.

Requirements - Current full-time enrollment in an accredited graduate degree program, or a qualifying degree earned within the past 12 months; the internship period may be up to one year. - At least a 3.0 cumulative GPA. - Graduate-level experience with foundation models and machine learning for time-series forecasting. - Experience pre-training or fine-tuning large AI models using high-performance computing. - Ability to provide school transcripts with the application; a recommendation letter is required if selected. - Must meet the relevant education requirements before the internship begins.

Nice to have - Experience with weather or energy forecasting. - Experience building agentic AI workflows. - Peer-reviewed research publications.

Benefits and work setup The internship is remote, fixed-term, and listed at 40 hours per week. Interns have access to experienced mentors, professional development, expert talks, and opportunities to connect with other interns. The listing describes health, dental, and vision coverage, a retirement savings plan with employer match, and sick leave, subject to eligibility.