The Company:
Gridmatic is a startup trying to help decarbonize the grid by using deep learning to forecast energy prices. We believe better forecasting can have a real-world impact on energy and climate. As extreme weather events get more common, energy prices become increasingly volatile — for instance, in the Texas energy market, prices can go 50x higher than usual in extreme scenarios. When this happens, the ability to forecast these price spikes becomes increasingly important.
We use our machine learning (ML) forecasting and optimization to trade in energy markets, make large-scale battery storage systems more efficient, and sell energy to businesses to protect them from extreme price volatility. Our deep learning models have proven very successful in trading in energy markets, we’re successfully operating multiple large batteries (50MW+), and we now sell energy to hundreds of businesses.
We have a very strong team with significant expertise in ML, energy, and optimization. If you’re interested in working on complex real-world problems, large-scale data challenges, and applying ML to climate and energy, we’d love to talk to you.
The Role:
We’re looking for an engineer to help lead the scaling and reliability of our data infrastructure, which is core to the ML work we do at Gridmatic.
Forecasting energy prices is challenging. We have very effective price forecasting models, but we’d like to go much further — scaling the amount of data we can use in our ML models by a factor of 10-100x by incorporating petabyte-scale weather data, increasing spatial granularity of our price forecasting, and more.
We’d also like someone who can tackle the challenge of scaling and improving reliability of our data platform. We deal with a lot of real-world problems when ingesting data from external sources — downtime, late-arriving data, changing schemas. Improving the reliability of our data pipelines will be critical to our ability to make an impact on the grid.
