Research and automation with real-world impact.

We apply engineering and research to speed up the adoption of renewable energy on the grid.
Careers

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
NextEra
Constellation
Vistra
NRG
Freepoint
ESG Global
Equilibrium
Software engineering
Google
Meta
Apple
LinkedIn
Samsara
Asana
Education
Stanford
MIT
Carnegie Mellon
UC Berkeley
Caltech
UCLA
Michigan
Northwestern

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 complex, 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
Logan Spear
"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
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