AI-driven energy optimization, forecasting and trading for the US grid
Gridmatic applies AI forecasting and optimization to energy storage, renewables and power-market trading in the US. Its stack spans GCP, AWS and Azure with a heavy streaming and orchestration layer (Kafka, Flink, Beam, Airflow, Flyte, Temporal), which points to real-time market data pipelines feeding trading models. Hiring is minimal and limited to a senior finance role, suggesting a steady-state team rather than rapid expansion.
Gridmatic is a privately held energy technology company founded in 2017 and headquartered in Cupertino, California. It models and optimizes electricity supply, demand and transactions to support clean energy adoption and grid stability in US energy markets. Its focus areas include energy storage, energy management, renewable optimization, energy trading and risk management, and predictive forecasting. The company employs 51–200 people and hires in the United States.
Gridmatic uses Python, Go, C++, Java and Rust on GCP, AWS and Azure, with Kubernetes, Terraform, Kafka, Apache Flink, Apache Spark, Apache Beam, Airflow, Flyte, Temporal, BigQuery, dbt and PostgreSQL.
Gridmatic is headquartered in Cupertino, California, and currently hires in the United States.
Hiring activity at Gridmatic is minimal, with recent openings concentrated in senior finance rather than engineering or sales.
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Gridmatic'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.