AI-Driven Power Systems Engineer Intern

GE Vernova, Inc.; Bellevue, Northern (WA, KY); Hybrid; USD 46,000 - 91,000; Full time

Benefits offered by this job

Relocation Assistance

Housing Allowance

Equal Opportunity Employer

Job summary

GE Vernova, Grid Solutions, seeks a graduate student intern for a 6–12 month project to advance AI/LLM use in electric utility network model validation, diagnostics, and remediation.

The role combines power systems knowledge with practical AI/LLM experience, focusing on AI-driven analysis, root-cause determination, and intelligent workflows for model remediation across utility domains.

Qualifications

• B.S or M.S. Electrical Engineering (Power Systems specialization) or Computer Science
• Strong analytical and problem-solving skills.
• Programming experience in Python and data analytics.
• Excellent English communication skills – both written and oral

Responsibilities

• Analyze extensive power system model validation and diagnostic logs.
• Develop AI/LLM-based approaches to detect, classify, and prioritize modeling issues.
• Investigate network model inconsistencies and determine probable root causes.
• Build intelligent workflows that prescribe suggestive or corrective actions for model remediation.
• Support and perform analysis, troubleshooting, and support of key Distribution Automation functions.
• Develop automated reasoning frameworks across GIS, SCADA/EMS, CIS, and Planning apps.
• Design prompt engineering and workflow-based solutions using modern LLMs.
• Evaluate AI-generated recommendations for accuracy and value.
• Collaborate with software engineers and power system experts to prototype solutions.

Skills

Strong analytical

Programming in Python

Excellent English communication

Education

B.S. Electrical Engineering (Power Systems) or MS Electrical Engineering (Power Systems) or Computer Science

Job description

GE Vernova, Grid Solutions, seeks a graduate student intern for a 6–12 month project to advance AI/LLM use in electric utility network model validation, diagnostics, and remediation.

The role combines power systems knowledge with practical AI/LLM experience, focusing on AI-driven analysis, root-cause determination, and intelligent workflows for model remediation across utility domains.