The problem
The construction industry loses $1.6 trillion a year to inefficiency, and most of it is avoidable. The knowledge that would prevent it already exists. It's just trapped in PDFs, meeting protocols, and email threads nobody can search. Every new project relearns what the last one already knew.
We're building the opposite of that: software for the people who run large, complex construction projects. It reads the documents, tracks the decisions, and catches the mistakes early. The model isn't the moat. The project memory is, and it compounds: every project makes the system better, and a competitor starting today is already years of projects behind.
Our software runs today on a live autobahn construction program and an S-Bahn transit program: multi-year timelines, hundreds of thousands of pages of specs, protocols, and site communications, and real consequences when we get an answer wrong.
We're pre-seed, led by Realyze Ventures, whose LPs include Zech and other large European construction groups. Co-investors: D11Z, the family office behind Aleph Alpha, and the CDTM Venture Fund, backed by 300+ CDTM alumni including the founders of Personio and Alasco and DeepMind's Technical Director. 25+ live customers.
Tasks
What you'd work on
Two hard problems, both in production.
AI-native project management, not a chatbot on PDFs. You build the product project managers live in every day: the dashboards, task views, and alerts. The AI belongs in the background, not in a sidebar you talk to, so you're building surfaces where automation runs on cronjobs, recommendations surface for the PM to approve, and some tasks run end to end with the user only reviewing after.
The data layer under a five-year project. Construction data is deeply interconnected: documents, protocols, decisions, tasks, and stakeholders all cross-reference each other across timelines that run for years. You design the schema, write the queries, and build the APIs that hold that graph and still serve the product fast enough to feel instant, at hundreds of thousands of pages and 50 stakeholders per project.
Stack. TypeScript, Next.js, Vercel, Supabase (Postgres + pgvector), LangChain, Vercel AI SDK, LangFuse, shadcn/ui. Every engineer gets €500/month for AI tooling: Claude Code, Cursor background agents, and whatever frontier model you want to test. No legacy. Greenfield.
How we work
- We ship at 80% and improve in the next iteration. Our users are on live infrastructure projects, so we learn from production.
- We'd rather you read the paper than reach for the nearest library. Most of what we build doesn't have a library yet.
- Feedback is direct and it's about the work. We move too fast to be precious about it.
- High ownership. If you spot the problem, it's yours to fix.
Requirements
Who we're looking for
You reason from user pain to solution to measurable outcome, and you can sit across from a non-technical customer and understand how they actually work.
You're a strong full-stack engineer. You build complex, performant frontends in TypeScript and React/Next.js and the backend APIs behind them, and you have real instincts for database design and query optimization, because construction data is deeply interconnected.
You prototype fast and measure everything. You also care about reliability, because on a live construction project, wrong has consequences.
Level is open. We've watched new grads outrun staff engineers and the other way around. If you're exceptional, we'll build the scope around you.
Nice to have, not required: comfort integrating AI features even if you don't build the models yourself, strong open-source work, an earlier stint as an early employee at a startup, domain depth in construction or complex B2B workflows, or a good eye for design so you can build clean interfaces without detailed mockups.
We work in English. German helps for customer calls but isn't required. We have native speakers for that.
Benefits
The offer
You'll be one of our first engineers, which means you own critical parts of the product end to end and work directly with the founders. It's early enough that you help decide how the company works, and the path from here goes to either top individual contributor or VP Engineering, depending on where you're strongest.
Equity: 0.5% to 1.5%, four-year vest, 1.5-year cliff.
Hybrid: three days a week in our central Munich office. If that's a dealbreaker for you, let's talk.
How hiring works
Three stages, no endless loops.
- A 30-minute call with one of our engineers.
- A 30-minute call with Vinzenz, our technical co-founder.
- An onsite case study in Munich. We send the materials ahead (plan on about 4 hours of prep), then we work through it together in the office. This is where we see how you think, debug, and ship under real conditions.
Offer decided within 24 hours of the case study, out within 48.
