Senior Backend & AI Engineer

Description

If you've built something before, start here

The hardest part of a startup usually isn't the code. It's finding customers who really need what you build and getting them to change how they work. At Scalera, that part is done: manufacturers, suppliers and contractors across Switzerland, Austria and Germany already run their tenders through us every day.

What we need now are people who can take the next unsolved problem and turn it into a product customers can't do without. If you've founded a company (whether it worked out or not), been the first engineer somewhere, or built something people paid for, this role was written for you.

About Scalera

Scalera is an ETH Zurich spin-off building AI for the €200B+ European construction industry. Large manufacturers and suppliers still run their most document-heavy processes by hand, from reading 300-page tenders to retyping purchase orders into their ERP. We automate them one by one. Our product matching already handles 1,400 tender positions in 16 minutes at 98% precision, work that used to take a sales rep most of a day.

Responsibilities

• You'll be part product manager, part engineer. You'll spend real time with customers and help shape what we build. Sometimes you'll spot the next problem yourself, and sometimes we'll hand you a clear priority to run with.
• Spend real time with customers. Join calls and site visits, sit with the people who do the work, and understand why they do it the way they do. You'll know their problems firsthand, not from a ticket.
• Shape what to build. Turn what you hear into a clear problem, a scope and a plan, and push back when the obvious request isn't the real need. When we bring you a priority, you'll own how it gets solved.
• Build it. Design and build backend services in Go and Python that turn messy tenders, catalogs and purchase orders into data customers can rely on.
• Ship, then check. Get it in front of customers fast, watch how they actually use it, and iterate.
• Own it in production. When something breaks at a customer, it's yours to understand and fix.
• Build with AI. Use AI heavily in how you work, and design systems that agents can use as well as people.
• What success looks like after 3 months
• There's a part of the product people now think of as yours, and you're the one deciding what happens to it next
• You've spotted a problem nobody asked you to solve, made the case for it, and shipped a first version
• You've shipped features from both sources: priorities we set and ideas from your own customer conversations
• At least once, you've talked us out of building something customers didn't really need
• You've left a part of the platform simpler than you found it
• You'll probably thrive here if
• You've taken something from zero to one: a company, a product line, or a side project with paying users
• You've built and run backend systems in production that other people depend on. We care more about your judgment than your language; you're strong in Go or Python and happy to work in both
• You're solid on data modeling, API design and messy real-world data
• You'd rather talk to a customer than wait for a spec
• You're comfortable with ambiguity. You can make progress while the problem is still half-defined, which at a startup it usually is
• You want a large scope of responsibility and are happy to own things well beyond your job title
• Rapidly changing requirements don't throw you. When customers or priorities shift, you adapt and keep going
• You enjoy direct customer contact and want to be on customer calls
• You make things simpler over time, not more clever
• You write clearly in English. German is a plus, not a requirement
• You probably won't enjoy it if
• You just want to code and go home
• You don't like talking to customers, whether on calls or on the occasional site visit
• Ambiguity and chaos bother you
• You want a well-defined backlog handed to you
• Your main goal is managing people soon. Here, scope grows with what you own, not with headcount
• Our stack
• Go and Python (FastAPI), PostgreSQL, GCP, and LLMs throughout the document pipeline. You don't need to know all of it on day one.