Founding AI Engineer - Full-Stack

The language requirements for this position are: German - Conversational, English - Fluent., 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.

Since you own things end to end, that includes the frontend. That means being comfortable not only with the AI engineering side, but with TypeScript and React too.

You prototype fast and measure everything. You also care about reliability, because on a live construction project, wrong has consequences.

You're comfortable without a map. Most of these problems don't have answers yet, so you read papers, build prototypes, and compare approaches instead of waiting for a best practice to show up.

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: serious open-source work, an earlier stint as an early employee at a startup, real depth in document understanding or agent systems, or something you shipped that replaced hours of human labor.

We work in English. German helps for customer calls but isn't required. We have native speakers for that., This role suits an AI engineer who wants to join at the earliest stage and build a product that changes how an entire industry works., * Real AI engineering experience. You've built with LLMs in production (agents, multi-step pipelines, retrieval, prompt and harness work) and you understand why shipping reliable agents is the hard part.

• Full-stack in TypeScript and React/Next.js. You own features end to end, from the agent logic down to the interface the customer actually uses.
• Product sense. You reason from user pain to solution to measurable outcome, and you can talk to a non-technical customer and understand their workflow.
• Velocity with judgment. You turn feedback into shipped features fast and make pragmatic architecture calls, but you care about reliability, because on a live construction project wrong has consequences.
• Comfort without a map. Most of these problems don't have answers yet, so you read papers, prototype, and compare approaches instead of waiting for a best practice.

Nice-to-haves

• Startup experience, ideally as an early employee with a broad remit.
• Domain depth in construction, manufacturing, or complex B2B workflows.
• Strong database instincts: schema design, query optimization, Postgres/pgvector. Construction data is deeply interconnected.
• Open-source work or writing you're proud of.
• Design sense. You can build a clean, usable interface without detailed mockups.
• German for customer conversations. Not required, we have native speakers for that.

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. What you'd work on Two problems, both of which need state-of-the-art answers. Agent harness engineering for construction documents. One summary doesn't fit all. "Structural risk" means something different in an RFI, a cost review, and a schedule reconciliation. We build multi-agent harnesses (specialized extraction, reasoning, and evaluation stages) that route a 400-page tender document or a protocol archive into the right pipeline with the right context. Part of that is context compression: what the system should remember, forget, and surface, and at which decision point, when a project runs five years and touches 50 stakeholders. There's no clean top-k answer, so this gets solved in production, not in a library. If you've read what Anthropic and Vercel have written about agent harnesses and thought "yes, that's the hard part of shipping production agents," this is the job. Project memory as a compounding moat. We started with meeting transcripts. Now we're building a decision graph that grows with every project: not just what was decided, but why, by whom, against which alternatives, and how it played out. That graph feeds the next project. The hard part is reconstructing the "why" when it's buried in a messy German protocol. We want the person who finds that problem interesting. 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., Alago is a Munich-based AI startup for the construction industry. With over €1M in funding, we're building an AI platform that catches mistakes in construction projects early and brings transparency to complex project workflows.