Research and automation with real-world impact.
We believe engineering and research can help speed up the adoption of renewable energy on the grid.

Our team.
We've built a team with deep expertise in energy, ML, and optimization — including researchers, energy traders, and practitioners with decades of experience in the energy industry. Our backgrounds include:
Energy
Shell
Constellation
ESG
NextEra
Vistra
Equilibrium Energy
Software engineering
Google
Meta
Apple
LinkedIn
Samsara
Asana
Education
Stanford
MIT
UC Berkeley
Northwestern
Carnegie Mellon
Our technical principles.
High-leverage engineering
Instead of relying on product managers or designers, engineers figure out what they need to build, and have ownership to build end-to-end, from concept to infra to product design.
Fast iteration cycles
We value pragmatic builders who can quickly ship something useful, and then rapidly iterate to make it better.
Domain expertise
The energy domain is incredibly deep, requiring understanding of data, market mechanics, weather, power transmission, telemetry, and much more. Most don't start with any energy experience, but learn quickly.
Making climate impact
We think a small team can make a dent in the clean energy transition. Our goals are rooted in the increased use of renewables, and we regularly read and discuss trends in climate and energy.
Quantitative rigor
We bring quantitative financial techniques to the energy domain — for instance, running reproducible backtests and optimizing for risk scenarios.
Automating the hard stuff
The energy industry is particularly difficult to automate, with archaic systems and lots of manual process. We see this as an opportunity and a necessary step toward cheap, abundant energy.
Our stack.
Infra
Google Cloud
Kubernetes
Terraform
Flyte, Temporal
Datadog
Prometheus, Grafana
Code
Python
Typescript
React, NextJS
Go
Data
BigQuery
Postgres
DBT
ML
Pytorch
Weights & Biases

"It's really dynamic. The problems we're working on are complex and constantly evolving, so we're always in learning mode — reading papers, running experiments, trading ideas."
Logan Spear, ML engineer
Contact
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