Posting TitleGraduate Intern - Power Data Analysis for AI Data Centers
LocationCO - Golden
Position TypeIntern (Fixed Term)
Hours Per Week40
Working at NLRNLR is located at the foothills of the Rocky Mountains in Golden, Colorado is the nation's primary laboratory for energy systems research and development.
Join the National Laboratory of the Rockies (NLR), where world-class scientists, engineers, and experts are accelerating energy innovation through breakthrough research and systems integration. From our mission to our collaborative culture, NLR stands out in the research community for its commitment to an affordable and secure energy future. Spanning foundational science to applied systems engineering and analysis, we focus on solving complex challenges to deliver advanced, secure, reliable, and cost-effective energy solutions. Our work helps strengthen U.S. industries, support job creation, and promote national economic growth.
At NLR, you'll find a mission-driven environment supported by state-of-the-art facilities, multidisciplinary research teams, and strong collaborations with industry, academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being.
Job DescriptionThe Digital Twins and Scalable Workflows group in NLR's Computational Science Center has an opening for a graduate student researcher in data center power analytics, with an emphasis on the electrical behavior of AI workloads. AI is driving rapid growth in data center electricity demand, and the sub-second power swings of GPU workloads are a growing concern for grid stability. This project measures the electrical power of GPU racks on NLR's Kestrel supercomputer while AI training, inference, and agentic workloads run, and uses those measurements to build models that predict realistic power behavior for workloads that haven't been measured.
The researcher will be the project's eyes on the data: reviewing the power measurements from every experiment, identifying anomalies and tracing their causes through system telemetry and profiling data, and labeling power ramps and other critical events. The researcher will also build an automated event detector, trained and evaluated on their own labeled data, that flags ramps and anomalies in new measurements.
We are looking for a curious, detail-oriented researcher with strong data analysis skills and an interest in energy, computing, and the grid. The successful candidate will work closely with NLR researchers in high-performance computing, power systems, and data center operations, and will have the opportunity to present and publish their work.
Responsibilities include:
- Review power measurements from AI workload experiments for anomalies and data quality issues, using an interactive visualization workbench.
- Hand-label power ramps, load steps, and other critical events, and identify regions that exemplify characteristic workload behavior, following a documented labeling protocol.
- Investigate unexplained behavior by correlating power measurements with GPU telemetry, profiling data, job logs, and system configuration, and document the findings.
- Design, build, and evaluate an automated detector for power ramps and anomalies, trained and tested on labeled data.
- Present results and contribute to technical reports, papers, and conference presentations.
Basic QualificationsMinimum 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 QualificationsPreferred Qualifications- Careful attention to detail, and the ability to follow and help refine a structured review process.
- Excellent written and verbal communication skills, including clear documentation of findings.
- Ability to work independently and to raise questions early.
- Experience analyzing time-series or signal data (for example, spectral analysis or event detection).
- Familiarity with GPU computing, AI/ML workloads, or HPC systems (for example, Slurm).
- Programming experience in Python for data analysis.
- Experience with machine learning for classification or detection.
- Background in power systems or electrical measurements.
- Experience with data labeling, annotation, or data quality review.
Job Application Submission WindowThe 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: $44,500 - $71,200
NLR takes into consideration a candidate's education, training, and experience, expected quality and quantity of work, required travel (if any), external market and internal value, including seniority and merit systems, and internal pay alignment when determining the salary level for potential new employees. In compliance with the Colorado Equal Pay for Equal Work Act, a potential new employee's salary history will not be used in compensation decisions.
Benefits SummaryBenefits include medical, dental, and vision insurance; 403(b) Employee Savings Plan with employer match*; and sick leave (where required by law). NLR employees may be eligible for, but are not guaranteed, performance-, merit-, and achievement- based awards that include a monetary component. Some positions may be eligible for relocation expense reimbursement. Internships projected to be less than 20 hours per week are not eligible for medical, dental, or vision benefits.
* Based on eligibility rules
Badging RequirementNLR is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as required by Homeland Security Presidential Directive 12 (HSPD-12), which includes a favorable background investigation. Intern assignments extending beyond six months will be subject to this requirement.
Drug Free WorkplaceNLR is committed to maintaining a drug-free workplace in accordance with the federal Drug-Free Workplace Act and complies with federal laws prohibiting the possession and use of illegal drugs. Under federal law, marijuana remains an illegal drug.
If you are offered employment at NLR, you must pass a pre-employment drug test prior to commencing employment. Unless prohibited by state or local law, the pre-employment drug test will include marijuana. If you test positive on the pre-employment drug test, your offer of employment may be withdrawn.
Submission GuidelinesPlease note that in order to be considered an applicant for any position at NLR you must submit an application form for each position for which you believe you are qualified. Applications are not kept on file for future positions. Please include a cover letter and resume with each position application.
Equal Opportunity EmployerAll qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws.
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