Senior AI/ML Engineer

<p style="min-height:1.5em"><strong>This role is also open to candidates living in the EU or EEA. We can't consider applications from other locations.</strong></p><p style="min-height:1.5em"></p><h2>If you've built something before, start here</h2><p style="min-height:1.5em">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.</p><p style="min-height:1.5em">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.</p><p style="min-height:1.5em"></p><h2>About Scalera</h2><p style="min-height:1.5em">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.</p><p style="min-height:1.5em"></p><h2>What you'll own</h2><p style="min-height:1.5em">You'll own the ML behind our products, end to end. In practice that means:</p><ul style="min-height:1.5em"><li><p style="min-height:1.5em"><strong>Training and fine-tuning</strong>: embedding models, rerankers, classifiers and LLMs, trained on our own data.</p></li><li><p style="min-height:1.5em"><strong>Document understanding</strong>: models that pull structure, quantities and product specs out of tenders, orders and catalogs.</p></li><li><p style="min-height:1.5em"><strong>Product matching</strong>: retrieval and LLM agents that match tender positions to the right products.</p></li><li><p style="min-height:1.5em"><strong>Evaluation</strong>: offline benchmarks, per-customer evals and gates that keep a new model from shipping if it makes things worse.</p></li><li><p style="min-height:1.5em"><strong>Training and serving infrastructure</strong>: automated retrain pipelines, GPU serving and vector search in production.</p></li><li><p style="min-height:1.5em"><strong>Customer problems</strong>: when a model gets something wrong for a customer, finding out why and fixing it.</p></li></ul><p style="min-height:1.5em"></p><h2>What success looks like after 3 months</h2><ul style="min-height:1.5em"><li><p style="min-height:1.5em">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.</p></li><li><p style="min-height:1.5em">You've spotted a problem nobody asked you to solve, made the case for it, and shipped a first version.</p></li><li><p style="min-height:1.5em">A model you trained runs in production, and customers can see the difference in their results.</p></li><li><p style="min-height:1.5em">Retraining runs automatically, and nothing reaches customers without passing an eval you designed.</p></l