{"id":3690736,"url":"https://alion.io/job/scalera-ag-senior-alml-engineer","title":"Senior AL/ML Engineer","company":{"id":481,"name":"Scalera AG","domain":"ra-scale.ai","url":"https://alion.io/company/scalera-ag","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Zurich, Switzerland"],"countries":["CH"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":100000,"max":120000,"currency":"CHF","period":"year","gross":null,"usd_annual":146241},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"GCP","optional":false},{"name":"LLM","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-11-03T16:25:31Z","employer_posted_date":null,"last_verified_at":"2026-11-03T16:25:31Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"If you've built something before, start here\nMost 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.\nWe 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.\nAbout Scalera\nScalera 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.\nHow we hire\n* Screening call (30 min) with our VP Engineering. It's two-way: ask us anything, including the hard questions.\n* Technical deep-dive call: walk us through a model or system you built, the decisions you made and what went wrong in production.\n* 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.\n* Offer: if it's a fit on both sides, we'll move quickly to an offer.\nWorking at Scalera\n* 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,\n* Occasional customer visits across Switzerland, Austria and Germany\n* Small team, no bureaucracy. You'll work directly with the CEO, CTO, VP Engineering and Customer Success\n* Competitive salary and meaningful equity\nFounders 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.\nRequirements\nWhat you'll own\nYou'll own the ML behind our products, end to end. In practice that means:\n* Training and fine-tuning: embedding models, rerankers, classifiers and LLMs, trained on our own data.\n* Document understanding: models that pull structure, quantities and product specs out of tenders, orders and catalogs.\n* Product matching: retrieval and LLM agents that match tender positions to the right products.\n* Evaluation: offline benchmarks, per-customer evals and gates that keep a new model from shipping if it makes things worse.\n* Training and serving infrastructure: automated retrain pipelines, GPU serving and vector search in production.\n* Customer problems: when a model gets something wrong for a customer, finding out why and fixing it.\nWhat success looks like after 3 months\n* 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.\n* You've spotted a problem nobody asked you to solve, made the case for it, and shipped a first version.\n* A model you trained runs in production, and customers can see the difference in their results.\n* Retraining runs automatically, and nothing reaches customers without passing an eval you designed.\n* You've cut LLM cost or latency by replacing an expensive step with a smaller model of your own.\n* You've decided at least one thing we should stop building.\nResponsibilities\nYou'll probably thrive here if\n* You've taken something from zero to one: a company, a model in production, or a side project with real users.\n* You've trained and fine-tuned models yourself, ideally embeddings, retrieval or document models, and evaluated them honestly on held-out data.\n* You've built with LLMs in production and know where agents fail.\n* You write strong Python and can run what you build: Docker, CI/CD, and cloud on GCP, AWS or Azure.\n* You measure before you believe, and a single aggregate score doesn't convince you.\n* You'd rather open a customer's actual document than wait for a spec.\n* You're comfortable with ambiguity. You can make progress while the problem is still half-defined, which at a startup it usually is.\n* You want a large scope of responsibility and are happy to own things well beyond your job title.\n* Rapidly changing requirements don't throw you. When customers or priorities shift, you adapt and keep going.\n* You enjoy direct customer contact and want to be on customer calls.\n* You write clearly in English. German is a plus, not a requirement.\nYou probably won't enjoy it if\n* You just want to code and go home.\n* You don't like talking to customers.\n* Ambiguity and chaos bother you.\n* You want a well-defined research agenda handed to you.\n* You'd rather hand a model to someone else to deploy and debug.\n* Your main goal is managing people soon. 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