Remote Power Systems Engineer - High-Fidelity Grid Modeling

Camus Energy Inc.; Campbell (CA); On-site; USD 180,000 - 230,000; Full time

Benefits offered by this job

Comprehensive benefits (FSA, 401k)

Fully remote workplace

Bay Area in-office option

Flexible PTO

Job summary

Camus Energy seeks a Power Systems Software Engineer to own and advance power systems modeling within the Camus platform. You will develop and validate high-fidelity models and drive scalable architectures for production-grade software.

You’ll work directly with utility customers to align data and validate outputs against real measurements, then deploy models in our cloud-native stack to enable large-scale planning and real-time operation of DERs.

Qualifications

• Masters or PhD in Electrical Engineering, Computer Science, or related field with 5+ years deploying power systems models.
• Strong power systems theory and optimization methods for distribution grids.
• Experience handling large utility datasets (GIS, SCADA, AMI).
• Proficiency translating math into production-ready code, especially Python.

Responsibilities

• Develop and maintain high-fidelity power flow and OPF models.
• Create tests and validation pipelines for model accuracy and reliability.
• Architect solvers balancing accuracy and performance.
• Build tooling to visualize model outputs for internal and client use.
• Collaborate with utility customers to validate data and model outputs.
• Integrate utility models (GIS, ADMS, time series) into the Camus platform.
• Identify and resolve bottlenecks in large-scale grid simulations.

Skills

Power systems

Python

Optimization

Team collaboration

Problem solving

Education

Masters/Ph.D. in Electrical Engineering or CS

Tools

OpenDSS

GCP

Job description

Camus Energy seeks a Power Systems Software Engineer to own and advance power systems modeling within the Camus platform. You will develop and validate high-fidelity models and drive scalable architectures for production-grade software.

You’ll work directly with utility customers to align data and validate outputs against real measurements, then deploy models in our cloud-native stack to enable large-scale planning and real-time operation of DERs.