alagoai
alagoai9mo ago
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Founding AI Engineer

GermanyGermany·MunichEmployeemid
Machine Learning EngineerData
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Quick Summary

Key Responsibilities

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,

Technical Tools
Machine Learning EngineerData

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.

Two problems, both of which need state-of-the-art answers.

  • 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

~1 min read

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.

What We Offer

~1 min read

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.

Three stages, no endless loops.

  1. A 30-minute call with one of our engineers.
  2. A 30-minute call with Vinzenz, our technical co-founder.
  3. 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.

Location & Eligibility

Where is the job
Munich, Germany
On-site at the office
Who can apply
DE

Listing Details

Posted
September 26, 2025
First seen
July 13, 2026
Last seen
July 14, 2026

Posting Health

Days active
0
Repost count
1
Trust Level
14%
Scored at
July 13, 2026

Signal breakdown

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alagoaiFounding AI Engineer