The construction industry loses $1.6 trillion annually to inefficiencies —
not because people are careless, but because critical knowledge is trapped
in PDFs, transcripts, and scattered systems. Every new project starts from
scratch.
We're building the opposite: an AI co-worker that extends the
Projektsteuerer (the person who runs complex construction projects),
while every project makes our system measurably better. The moat isn't the
model — it's the compounding project memory no competitor can replicate.
Pre-seed led by Realyze Ventures — LPs include Zech and other major
European construction groups. Co-investors: D11Z (the family office behind
Aleph Alpha) and the CDTM Venture Fund (backed by 300+ CDTM alumni
including founders of Personio, Alasco and the Technical Director of
DeepMind).
Our software is running today on a major autobahn construction
program and an S-Bahn transit program — multi-year timelines,
hundreds of thousands of pages of specs, protocols, and communications.
Real consequences when we get it wrong.
Tasks
What you'd actually work on
1. Agent harness engineering for construction documents
One summary doesn't fit all. "Structural risk" in an RFI means
something different than in a cost review, which means something
different in a schedule reconciliation. We build multi-agent
harnesses — specialized extraction, reasoning, evaluation stages
— that route 400-page tender documents and protocol archives to
the right pipeline with the right context.
If you've read Anthropic's or LangChain's writing on agent
harnesses and thought "yes, that's the hard part of shipping
production agents" — that's this job.
2. Project memory as a compounding moat
We started with meeting transcripts. We're building a decision
graph that grows with every project — tracking not just what was
decided, but why, by whom, against which alternatives, and with
what outcome. That graph feeds the next project. Every closed
workflow makes the next one faster.
This is the operational-continuity layer no ConTech player is
building. We want someone who gets excited that the hard part
here is not retrieval — it's deciding what signal to keep.
3. Context compression for 5 year projects
What should the system remember? Forget? Surface at which
decision point? There's no clean top-k answer when a project
spans 5 years and touches 50 stakeholders. This is an open
research problem we're solving in production — and we'd rather
hire someone who reads papers than someone who installs
libraries.
Stack: TypeScript, Next.js, Vercel, Supabase (Postgres + pgvector),
LangChain, Vercel AI SDK, LangFuse, shadcn/ui. €500/month AI tooling
budget per engineer — Claude Code, Cursor background agents,
experimentation with frontier models. No legacy. Greenfield.
Requirements
Who we're looking for
Product sense: you reason from user pain → solution →
measurable outcome. You can talk to non-technical customers
and understand their workflows.
Velocity + craft: prototype fast, measure everything, iterate on
real feedback. But you care about reliability because the downside
of wrong is real in construction.
Comfort with the unknown: many of these problems don't have
Stack Overflow answers. You read papers, prototype, and
compare approaches.
Bonus: strong open-source work, previous early-employee
startup experience, domain depth in document understanding or
agent systems, or shipped systems that replaced hours of human
labor.
Level is opportunistic: We've seen brilliant new grads
outperform staff engineers and vice versa. If you're exceptional,
we'll find the right scope.
Language: We work in English. German is nice-to-have for
customer conversations but not required — we have native
speakers for that.
Benefits
As one of our first hires, you’ll do more than contribute — you’ll help shape
how Alago works: our culture, systems, and growth strategy. You’ll work
directly with the founders, gain exposure to every function, and ship
projects that have immediate impact.
10× Learning Curve. Work with cutting-edge AI to tackle
real-world challenges every day
Work directly with the founders. You’ll own critical parts of alago
end-to-end, laying the technical foundation while balancing rapid
iteration, customer value, and long-term scalability.
Hybrid: A vibrant in-office culture in our central Munich
office. 3 days per week in our central Munich office, flexible
otherwise.
Meaningful equity stake: 0.5% – 1.5% (4-year vest, 1.5-year
cliff)
Your needs and well-being matter to us. You’ll get access to
sponsored EGYM Wellpass to find inner peace at yoga or kill it at
HIIT workouts.
We’re building for scale — but right now, it’s still early. That means lots of
autonomy, tight feedback loops, and the freedom to grow into whatever role
suits your strengths, whether that’s become top individual contributor or
stepping into leadership roles like VP Engineering.
£70,000 - £90,000/year
4 days ago
Munich - On Site, To Be Confirmed