Posting Title

Graduate Summer Intern – AI/ML Software Developer

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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 graduate intern will be part of a larger team developing sensor placement and optimization software tools for modern distribution power systems integrated with clean energy resources such as Photovoltaics, batteries, EV Charging Stations, and electric vehicles. The intern will contribute to the development of deep learning models such as Bayesian neural networks, graph neural networks, probabilistic semantic graphs, and Generative AI to detect anomalies such as missing data, bad data, and communications failure in sensor data. Development of data driven forecasting and disaggregation models and validation of both centralized and distributed state estimation algorithms.

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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

Preferred Qualifications

• Experience in deep learning models, Bayesian neural networks, graph neural network models, and probabilistic semantic graphs.
• Experience in the formulation of mixed-integer optimization problems and open-source solvers for sensor placement in distribution systems.
• Demonstrated experience in programming languages such as Python and MATLAB.
• Experience with open-source, collaborative software development and use of tools such as GitHub or GitLab for project management and version control.

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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: $43,700 - $69,900

NREL takes into consideration a candidate’s education, training, and experience, expected quality and quantity of work, required travel (if any), external