Power Systems Research Lead (Contract)

Skills

Job Specifications

Voltry is assembling a founding research team focused on next-generation power quality, harmonics intelligence, and grid-scale electrical behavior in AI-era infrastructure.

This role leads applied research into how modern compute-dense electrical loads interact with power systems, how disturbances propagate, and how these effects can be measured, modeled, and validated in real-world environments.

This is a research-first role with direct relevance to utility-scale grids, data centers, and standards-driven power systems work.

What You’ll Work On
Research leadership across power quality, harmonics, distortion, and non-linear load behavior
Analysis of electrical disturbances across complex power systems
Design and validation of measurement approaches for grid-scale power behavior
Translation of research findings into pilot programs, validation studies, and external collaborations
Collaboration with signal processing, data, and machine learning researchers
Engagement with external research institutions and standards bodies where appropriate

Background We’re Looking For
PhD or postdoctoral experience in power systems engineering or electrical engineering
Deep familiarity with power quality, harmonics, and grid behavior under modern loads
Experience with applied research, modeling, and experimental validation
Ability to operate independently in an early-stage research environment
Prior exposure to grid-scale systems, utilities, or large electrical infrastructure preferred

Contract Details
Contract research position (paid)
April 1, 2026 – September 30, 2026 (6 months)
15–25 hours per week
Remote-first

**This role may include occasional travel to a U.S. national research laboratory (e.g., NREL, Golden, CO) for collaborative research and validation activities.

About the Company

Voltry gives data-center operators real-time visibility into the health and stability of their power. We use AI to interpret the electrical and acoustic “noise” that others filter out, predicting downtime, equipment stress, and compliance issues before they happen. Everyone else cleans the noise out. We learn from it. Know more

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