Full Stack Engineer Hybrid; Cambridge, MA

Location: Boston

Type: Full-time, early technical hire

• Customer-Facing Interface:
  Help design and build the interface where customers explore building data, view energy simulations, and act on upgrade recommendations.

• Data Pipelines & Integrations:
  Connect and normalize data from diverse sources (satellite imagery, utility records, building permits, economic data) into a unified format our models can use.

• API Development:
  Extend our FastAPI backend to serve new data products and support frontend features.

• Simulation Workflow:
  Contribute to the asynchronous job systems that run energy simulations at scale across thousands of buildings.

Month 1:
Orientation & UI

• Get up to speed on the core stack (Python, FastAPI, PostgreSQL, AWS).

• Take ownership of your first front-end components and ship a customer facing feature to production.

Month 2:
Start getting into the backend and data pipelines

• Extend the API to support new data visualizations and customer workflows based on customer feedback and input.

• Wrangle messy datasets: cleaning, formatting, and integrating sources that may require custom web scrapes, pdf parsing, etc. across different location granularities (e.g., county, state, federal).

Month 3: E2E ownerships

• Own a feature from API design through frontend deployment.

• Contribute to the feedback loop: how we ingest customer input and use it to improve outputs.

• You have a deep understanding of our full analytics platform and can navigate the codebase with confidence.

• You are shipping high-quality code across the entire stack, from frontend components to backend logic and AI integrations.

• Engineering velocity is high because you are a proactive problem solver who can unblock yourself and others.

• Data normalization: Buildings don't come with clean datasets. You'll figure out how to reconcile mismatched addresses, incomplete utility records, and inconsistent permit data into something usable.

• Making dense outputs legible: Energy simulations produce a lot of numbers. You'll build interfaces that help non-technical users understand what matters and what to do next.

• Fast iteration with real customers: We work directly with our users to improve our products, and want to ensure we are always developing new features that align with their vision.

• Built and deployed web applications using Python and a modern JS framework (Svelte, React, Vue, or similar).

• Designed REST APIs and worked with relational databases (PostgreSQL or equivalent).

• Wrangled imperfect data—cleaning, transforming, or integrating datasets from multiple sources.

• Shipped something end-to-end, whether through internships, personal projects, or open-source work.

• CS degree or equivalent experience (new grads welcome!).

• Early team ownership
  :
  You’ll join an experienced founding team—a former McKinsey partner, a technical founder with deep energy expertise, and an experienced operator.

• Greenfield + real traction
  :
  We’ve proven the MVP and have paying customers—now we’re scaling to thousands of buildings.

• Surface area that matters
  :
  Every feature you build directly contributes to bending the curve down on global emissions.

• Intellectual Curiosity
  : A desire to learn the "why" behind building energy systems and AI models.

• Pragmatic rigor
  :
  Measure, ship, iterate.

• Low-ego collaboration
  :
  Teach, learn, and write things down.

Competitive salary + potential for equity (early-engineer level).