Graduate Intern – Power Data Analysis for AI Data Centers

National Laboratory of the Rockies • Golden, CO

10d • Onsite

About The Position

The Digital Twins and Scalable Workflows group in NLR’s Computational Science Center is seeking a graduate student researcher to analyze data center power, with an emphasis on the electrical behavior of AI workloads. The project measures the electrical power of GPU racks on NLR’s Kestrel supercomputer during AI training, inference, and agentic workloads. Those measurements will be used to develop models that predict realistic power behavior for workloads that have not yet been measured.

The researcher will review power measurements from experiments, identify anomalies and investigate their causes using system telemetry and profiling data, and label power ramps and other critical events. They will also build and evaluate an automated detector for power ramps and anomalies using their labeled data. The researcher will work with NLR staff in high-performance computing, power systems, and data center operations, and may present and publish their work.

This internship is based in Golden, Colorado, and is listed at 40 hours per week. NLR describes its work as energy systems research and development and offers a mission-driven environment, state-of-the-art facilities, multidisciplinary research teams, collaborations, professional development opportunities, and a competitive benefits package. Applicants must meet the stated educational requirements before the employment start date. Intern assignments extending beyond six months are subject to the stated PIV card and favorable background investigation requirement.

Requirements

• Minimum cumulative GPA of 3.0.
• For the graduate student pathway, be enrolled as a full-time student in a master’s degree program at an accredited institution; the posting also describes eligibility for students who have completed a master’s degree within the past 12 months or completed a master’s degree and are enrolled as a PhD student at an accredited institution.
• Meet educational requirements before the employment start date.
• Careful attention to detail and 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 raise questions early.

Nice To Haves

• Experience analyzing time-series or signal data, such as spectral analysis or event detection.
• Familiarity with GPU computing, AI/ML workloads, or HPC systems such as 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.

Responsibilities

• 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 according to a documented labeling protocol.
• Investigate unexplained behavior by correlating power measurements with GPU telemetry, profiling data, job logs, and system configuration, and document 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.

Benefits

• Medical insurance.
• Dental insurance.
• Vision insurance.
• 403(b) Employee Savings Plan with employer match.
• Sick leave where required by law.
• Potential eligibility for performance-, merit-, and achievement-based awards with a monetary component; awards are not guaranteed.
• Some positions may be eligible for relocation expense reimbursement.
• Professional development opportunities.

What This Job Offers

• Career Level: Intern