Senior AL/ML Engineer

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