Senior AL/ML Engineer

Salary: CHF 100'000 - 120'000 per year

Requirements:

What you'll own

You'll own the ML behind our products, end to end. In practice that means:

* Training and fine-tuning: embedding models, rerankers, classifiers and LLMs, trained on our own data.

* Document understanding: models that pull structure, quantities and product specs out of tenders, orders and catalogs.

* Product matching: retrieval and LLM agents that match tender positions to the right products.

* Evaluation: offline benchmarks, per-customer evals and gates that keep a new model from shipping if it makes things worse.

* Training and serving infrastructure: automated retrain pipelines, GPU serving and vector search in production.

* Customer problems: when a model gets something wrong for a customer, finding out why and fixing it.

What success looks like after 3 months

* There's a model or part of the pipeline 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.

* A model you trained runs in production, and customers can see the difference in their results.

* Retraining runs automatically, and nothing reaches customers without passing an eval you designed.

* You've cut LLM cost or latency by replacing an expensive step with a smaller model of your own.

* You've decided at least one thing we should stop building.

Responsibilities:

You'll probably thrive here if

* You've taken something from zero to one: a company, a model in production, or a side project with real users.

* You've trained and fine-tuned models yourself, ideally embeddings, retrieval or document models, and evaluated them honestly on held-out data.

* You've built with LLMs in production and know where agents fail.

* You write strong Python and can run what you build: Docker, CI/CD, and cloud on GCP, AWS or Azure.

* You measure before you believe, and a single aggregate score doesn't convince you.

* You'd rather open a customer's actual document 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 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.

* Ambiguity and chaos bother you.

* You want a well-defined research agenda handed to you.

* You'd rather hand a model to someone else to deploy and debug.

* Your main goal is managing people soon. Here, scope grows with what you own, not with headcount.

Technologies:

AI

AWS

Azure

CI/CD

Cloud

CTO

Docker

Embedding Models

ERP

Fine-tuning

GCP

LLM

Model Serving

Python

Agentic AI

Langfuse

PostgreSQL

More:

If you've built something before, start here

Most ML jobs hand you a benchmark. This one hands you a business problem with paying customers and their real data. Construction tenders are long, messy and full of trade jargon, and every decision a customer makes on our platform becomes a labeled example you can train on.

We need someone who can turn that data into models that customers notice: from the first experiment to the model serving production traffic, and back again when a customer says it got something wrong. If you've founded a company (whether it worked out or not), been the first ML engineer somewhere, or shipped a model 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. Manufacturers, wholesalers and suppliers in Switzerland, Austria and Germany still run their most document-heavy work by hand, from reading 300-page tenders to retyping purchase orders into their ERP. We automate that work one process at a time, and ML sits at the center of every one of them.

How we hire

* Screening call (30 min) with our VP Engineering. It's two-way: ask us anything, including the hard questions.

* Technical deep-dive call: walk us through a model or system you built, the decisions you made and what went wrong in production.

* Meet Scalera Day: join us for a day at our office in Zürich, work on a real problem with real (anonymized) tender data, and meet the team.

* Offer: if it's a fit on both sides, we'll move quickly to an offer.

Working at Scalera

* Zürich HQ, four days in the office, Fridays remote. If not located in Zürich then this role is open to remote within the EU or EEA,

* Occasional customer visits across Switzerland, Austria and Germany

* Small team, no bureaucracy. You'll work directly with the CEO, CTO, VP Engineering and Customer Success

* Competitive salary and meaningful equity

Founders and former founders: if you're unsure whether you fit, send a few lines about what you built and why you moved on. We read every one.

last updated 44 week of 2026