Lead Machine Learning Engineer

About Bondora At Bondora, our mission is clear: to empower people to enjoy life more while alleviating the stress of managing finances. Founded in 2008, Bondora has proudly served a diverse community of over 1 million customers for an impressive 16 years. As a rapidly growing financial technology company, we’re reaching new heights with a bold vision. We’re set to acquire a banking license, unlocking a world of possibilities for our customers. Our investment product is available Europe-wide, and we’re actively expanding our loan product footprint to 11 new countries. Join us on this journey and let’s build the future of finance together! What is this role about? As the Lead Machine Learning Engineer at Bondora, you will be the backbone of our Data Science delivery. Your mission is to build the robust infrastructure that powers automated model pipelines, ensures deployment reliability, and governs the full ML lifecycle from experimentation to production. This is a strategic and hands on engineering role. You will collaborate closely with Data Science, Data Engineering, and Development teams to remove friction, improve scalability, and bring stable, high quality ML solutions into everyday decision making. You will guide the evolution of our ML engineering stack, lead high impact initiatives, and mentor engineers while shaping a culture of technical excellence 🌿 Your main responsibilities 🎯 - Guide the technical direction of Bondora’s ML engineering stack by selecting, evaluating, and implementing technologies, tools, and processes that improve scalability and reliability. - Lead complex, high risk, or cross departmental projects that directly influence Data Science delivery, risk model performance, and production stability. - Act as the bridge between Data Science, Data Engineering, and Development to identify and solve systemic technical challenges. - Design and build advanced, production grade ML infrastructure and set the engineering standard for the team. - Ensure all ML solutions are secure, observable, resilient, and scalable, following governance, compliance, and operational best practices. - Mentor ML Engineers through code reviews, design sessions, and hands on technical leadership. - Identify weaknesses or inefficiencies in model or data infrastructure and drive company wide improvements. - Represent the ML Engineering team in technical discussions and communicate architectural decisions clearly to stakeholders.