We’ve grown our energy sales by roughly 100x in the last 18 months, and we want to 10x again to become a gigawatt-scale energy retailer, which would be a huge step toward becoming one of the leading renewable energy companies.

 

This will require building automation and tooling to make it exceptionally easy to sign increasingly large industrial energy customers (think datacenters, flexible loads, and other businesses). There’s a ton of complexity in the pricing, usage forecasting, and contracting of these customers, and we’re building a platform to automate this entire process. Incumbent energy companies need hundreds of salespeople; we want to see how much we can grow with fewer than ten.

 

We’re hiring for exceptional product engineers who can help build out this vision. We look for people who can ship and iterate quickly, and who are also adept at working with stakeholders to figure out what to build.

 

• Deep automation of our pricing, forecasting, and contracting platform. Examples include: using AI to fully automate pricing from unstructured emails, scaling the processing of large-scale timeseries usage data, and representing increasingly complex customer contracts.
• Rapid expansion into new markets. New markets expand our sales opportunity but requires handling new utilities, new datasets, and new pricing rules and algorithms.

 

You are a good fit if you: 

• Excel at building product end-to-end, from spec and data modeling to backend to frontend and product design. We work in an environment where every engineer has a huge amount of leverage and impact.
• Are an expert at learning and gathering requirements from stakeholders. We’re building new tools and products in the energy domain, and this requires very effectively with people who have energy experience.
• Are very comfortable working with complex, large-scale data. Energy data is often messy, so there’s a ton of value in being able to design good schemas, write queries, and make sense of the data.
• Have strong Python backend skills - being able to debug production issues, set up alerting and observability, scale large parallel workloads, and do performance profiling.
• Can thrive in a startup environment where there’s ambiguity and the need to manage your own priorities and focus.

 

This role requires candidates to adhere to our hybrid policy, 3 days a week in office with at least 1 day in our Cupertino office.