Senior Machine Learning Engineer

At Finom, we're transforming how small and medium businesses across Europe manage their money - and financial crime prevention sits right at the heart of that promise. As a Senior Machine Learning Engineer, you'll take over DRS: a production ML product that scores our whole client base daily and drives our quarantine, alert suppression, and soon our credit decisions. This is genuine end-to-end ownership on a small team - you'll deploy, serve, monitor, and improve the system rather than inherit a finished one. If you're the kind of engineer who quietly keeps production models honest and wants their decisions to actually reach live traffic, this is your seat.

What You Will Be Doing

• Full-stack ML model development (binary classification and regression) for the detection and prevention of financial crime during transaction processing and client onboarding: gathering requirements, prototyping, and deployment in a production environment.
• Own and evolve the Dynamic Risk Score (DRS) across all markets - daily scoring of the full client base that feeds decisioning across the client lifecycle.
• Handle a portfolio of transaction rules with ML-driven detection inside, optimizing their performance and the number of generated alerts.
• Set up continuous monitoring of model and rule performance in production, and develop the champion-challenger framework so the best-performing model is always the one live in production.
• Participate in infrastructure development for ML model operationalization and inter-service interactions, so models of varying complexity can be served and inferred.

Who You Are

• At least 5 years as a Machine Learning Engineer or Full-stack Data Scientist, with production deployment experience (incl. API services like FastAPI).
• Strong in SQL and Python, and with cloud data platforms (e.g. Databricks).
• Strong architectural skills - able to design and improve infrastructure that serves models of varying complexity.
• Experience with model monitoring and champion-challenger / A-B evaluation in production. Exposure to graph modeling or foundation models/embeddings is a plus.
• Critical, analytical thinker - proactive, result-driven, comfortable working with little supervision in a start-up setting.
• A quantitative education (math, engineering, economics, or CS) and full working proficiency in English.