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Onboarding is the first handshake a business has with Mollie, and we’re making it smarter. As a Machine Learning Engineer within our Discovery - Onboarding team, you’ll build the intelligent systems that turn a complex regulatory hurdle into a seamless, automated experience. Your work directly impacts our ability to scale by accelerating how quickly merchants can start processing payments safely. You’ll bridge the gap between data science and production-grade engineering. We aren’t looking for theorists; we need a builder who can design, deploy, and maintain ML models that solve real-world problems like document verification and risk assessment. By joining this mission, you’ll help us eliminate manual friction and define what the future of automated onboarding looks like in a high-growth fintech environment.
Job Responsibility:
Build and deploy production-ready ML models that automate merchant verification and onboarding workflows
Scalable data pipelines and feature stores that power real-time decision-making systems
Robust MLOps infrastructure to ensure our models are monitored, retrained, and performing at peak efficiency
Integrated AI solutions that reduce manual intervention, directly shortening the time-to-revenue for new merchants
Technical documentation and clean, modular code that sets the standard for ML engineering within the team
Requirements:
Solid experience putting machine learning models into production environments
Deep proficiency in Python and familiarity with modern ML frameworks like PyTorch, TensorFlow, or Scikit-learn
Strong engineering fundamentals with experience in SQL, cloud infrastructure (GCP preferred), and CI/CD practices
A results-driven mindset
A collaborative spirit, ready to work closely with Product and Risk teams