Sensor-based active grid response platform for utility grid monitoring
Gridware builds an active grid response platform pairing mechanical sensors on power infrastructure with cloud analytics to catch faults early. The stack spans LoRA, BLE and SCADA at the edge plus Kafka, Databricks, MLflow and PyTorch in the cloud, pointing to a hardware-plus-ML company now focused on IoT fleet uptime and predictive device health. Senior-heavy engineering hiring, plus finance and business development leadership, suggests a push toward commercial scale.
Notable leadership hires: Business Development Director
Gridware is a San Francisco-based, privately held company founded in 2020 that develops technology to protect and improve the electrical grid. Its active grid response platform monitors electrical, physical and environmental conditions affecting grid reliability and safety, using high-precision mechanical sensors to flag issues before they become failures. The platform supports proactive maintenance, fault mitigation and outage reduction for grid operators. The company has 51–200 employees and hires in the United States.
Gridware uses Python, C++, PyTorch, Keras and scikit-learn for ML, Kafka, Kinesis and Databricks for data, AWS and Kubernetes for infrastructure, and LoRA, Bluetooth Low Energy and SCADA for device connectivity.
Gridware is building out its active grid response platform, alongside signal compression techniques, an end-to-end firmware and hardware program, and predictive modeling for sensor fleet health.
Yes. Engineering is Gridware's largest hiring area, weighted toward senior roles, with additional openings in data, product, finance and business development, all based in the United States.
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Gridware's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.