Gridmatic uses ML and weather data to forecast energy prices in the US, which can get really volatile as extreme weather becomes more common with climate change. We've been using those price forecasts to e.g. optimize very large batteries, which help make the grid more stable. On the fullstack side, we're building tools to apply our forecasts to automated battery storage optimization and energy volatility risk mitigation. On the ML infra side, we're working on infra to be able to scale to larger and more complex models, and to apply better (but more computationally intensive) weather models in our forecasting. Our stack: python, react/nextjs, kubernetes, GCP, postgres, bigquery Our goal is to help accelerate the adoption of renewable energy on the grid. It's super interesting, the team is incredibly smart, and we're profitable.

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