Senior Machine Learning Engineer

About Finom

Finom is a European tech startup headquartered in Amsterdam, and we're on a journey towards revolutionizing the financial landscape for entrepreneurs worldwide. Our mission is to develop an all-in-one financial B2B solution that integrates banking functions, accounting, financial management, and invoicing into a seamless, mobile-first platform.

We recently closed a €115 million Series C equity round (around $133 million), bringing our total funding to approximately $346 million. This significant investment follows a $105 million growth funding round from General Catalyst, a long-term backer since 2021 known for supporting companies like Airbnb, Hub Spot, KAYAK, and Stripe.

Finom's platform goes beyond traditional banking, offering invoicing and a growing suite of features, including AI-enabled accounting, aiming to simplify financial management for entrepreneurs. We're actively expanding our reach across key EU markets like Germany, France, the Netherlands, Italy, and Spain.

At Finom, we're not just redefining the entrepreneurial experience - we're empowering our employees to make a real difference. Your work matters, and your impact extends far beyond product metrics. We nurture innovation and an inspiring work environment where bold ideas thrive, prioritizing thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and our business as a whole.

Maintaining our start-up spirit, we prioritize thorough research, swift implementation of solutions, and ensuring that every effort we make benefits our users, employees, partners, and, of course, our business.

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.

• 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.

• 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…