FOUNDING FULL-STACK ENGINEER (PYTHON/TYPESCRIPT)
About GoCanopy
GoCanopy is the AI-powered deal and asset management platform for institutional real estate investors. We help the world's largest allocators move faster on deal flow and asset management by turning unstructured data, documents, and workflows into structured, actionable intelligence.
We raised a €2.1M seed round led by ISAI, we are based at Station F in Paris, and we are building the category-defining product in a market that has barely been touched by AI. Our clients are global, our ambitions are global, and we plan to be the operating system for institutional real estate investors worldwide.
The role
We are looking for a Founding Full-Stack Engineer to join our small, high-leverage team and own end-to-end feature delivery on our core product. You will work directly with our CTO and a tight engineering group, shipping features that go straight into the hands of acquisition teams and asset managers at top-tier institutional investors.
This is a builder role, not a maintainer role. You will architect, write, deploy, and iterate fast.
Our stack
- Vue + TypeScript
- Node.js + TypeScript
- Python
Location
You can reliably be at our Station F office at least 2 days per week. Paris-based is ideal; nearby commuter-friendly regions work too.
What You\'ll Do
- Ship full-stack features end-to-end across the Vue frontend, the Node platform API, and the Python AI service.
- Build and improve the harness around our agents: execution loops, tool registries, context management, structured outputs, evals, observability. Make our AI workflows faster, cheaper, and more reliable on real client data.
- Translate complex real estate investment workflows into clean, fast product experiences.
- Own quality: write code that is fast, readable, and observable in production.
- Contribute to architectural decisions across the stack as we scale.
Preferred Experience
What we\'re looking for
- 5+ years of full-stack engineering experience, ideally in a fast-moving product environment (startup or scale-up).
- Strong proficiency in TypeScript across both Node.js (backend) and a modern frontend framework (Vue ideal, React or Svelte fine if you have shipped serious frontend work).
- Comfortable working in Python (FastAPI, Litestar, or similar) for our AI service.
- Solid grasp of Postgres, REST/GraphQL APIs, and cloud infrastructure.
- Genuine interest in agentic AI systems: you do not need to have shipped agents to prod, but you should be excited to go deep on harness engineering, evals, and how LLMs actually behave in production.
- A bias for ownership: you push features to production, you measure their impact, you iterate.
- Comfortable working in English; French is a plus but not required.
Bonus
- Hands-on experience with LLMs and agentic systems in production: tool calling, RAG, structured outputs, evals, observability.
- Background in real estate, finance, or B2B SaaS targeting institutional clients.
- A point of view on agent design, harness engineering, or eval-driven LLM development.
- Open-source contributions or side projects that show how you think.
What We Offer
- Competitive salary + meaningful equity. We want you to own a real piece of what we build.
- A front-row seat at one of the most ambitious vertical AI startups in Europe, backed by ISAI and serving the world\'s largest investors.
- A small team of high-output people. No politics, no committees, no busywork.
- Office at Station F, the world\'s largest startup campus, with a hybrid setup.
- Direct exposure to senior buyers and clients at top-tier institutions globally.
Recruitment Process
How To Apply
Send your CV / GitHub / LinkedIn to , together with short answers to the following:
- What is something you have shipped that you are proud of? Tell us what it was, what made it hard, and what you learned.
- What is your experience with LLMs in production? If you have shipped LLM features, tell us what worked and what broke. If you have not, tell us what you would build first.
Additional Information
- Contract Type: Full-Time
- Location: Paris
- Possible partial remote