(Senior) Machine Learning Engineer (m/f/d)

<p> </p> <p> </p> <p> </p> <p><span><span><strong>We are looking for a</strong></span></span></p> <p><span><span><strong>(Senior) Machine Learning Engineer (m/f/d)<br/>Full-time at our location in Berlin or Amsterdam - hybrid working conditions available.<br/></strong></span></span></p> <p> </p> <p><span><span>The Data Science (Consumer and Risk) team at Riverty is seeking a skilled Machine Learning Engineer to build and productionize ML models that power our decision-making for online payment products. Your mission will be to develop, deploy, and maintain scalable machine learning systems to help us detect fraud and assess customer creditworthiness in real time.</span></span></p> <p> </p> <p><span><span><strong>Your Responsibilities:</strong></span></span></p> <ul> <li><span><span>Design, build, and maintain end-to-end machine learning pipelines — from data ingestion and preprocessing to model deployment and monitoring.</span></span></li> <li><span><span>Develop scalable and robust machine learning solutions that power risk and fraud decisioning.</span></span></li> <li><span><span>Collaborate with data scientists to turn experimental models into efficient production-ready systems.</span></span></li> <li><span><span>Improve and optimize existing models, infrastructure, and workflows for reliability, performance, and maintainability.</span></span></li> <li><span><span>Conduct code reviews and contribute to our ML engineering best practices.</span></span></li> <li><span><span>Work closely with product managers, data engineers, and cross-functional teams to integrate ML models into customer-facing applications.</span></span></li> </ul> <p> </p> <p><span><span><strong>What You Bring:</strong></span></span></p> <ul> <li><span><span>Solid experience as a Machine Learning Engineer, MLOps Engineer, or similar role with a focus on deploying ML models at scale.</span></span></li> <li><span><span>Strong software engineering skills in Python, especially in the context of building and deploying ML solutions.</span></span></li> <li><span><span>Hands-on experience with Kubernetes for container orchestration and scalable ML deployments.</span></span></li> <li><span><span>Experience deploying models to production using frameworks such as MLflow, FastAPI, or similar.</span></span></li> <li><span><span>Familiarity with model monitoring, retraining strategies, and performance evaluation in a production setting.</span></span></li> <li><span><span>Proficiency with version control, CI/CD pipelines, and containerization tools like Docker.</span></span></li> <li><span><span>Understanding of working with imperfect, real-world datasets collected outside of typical ML workflows.</span></span></li> <li><span><span>A degree in Computer Science, Machine Learning, Engineering, or a related STEM field.Experience working with transactional databases or case handling systems.</span></span></li> </ul> <p> </p> <p><span><span><strong>Bonus Skills:</strong></span></span></p> <ul> <li><span><span>Experience with tools such as SQL, Spark, Databricks, VS Code, and Docker.</span></span></li> <li><span><span>Familiarity with cloud infrastructure (e.g., AWS, Azure, or GCP) for deploying machine learning systems.</span></span></li> <li><span><span>Prior exposure to the risk, fraud, or fintech domain.</span></span></li> <li><span><span>Knowledge of MLOps practices and tools for model lifecycle management.</span></span></li> </ul> <p> </p> <p><span><span><strong>Our Hiring Process</strong></span></span></p> <p><span><span>We aim to make our hiring process smooth and transparent:</span></span></p> <ul> <li><span><span>Pre-screening Call with HR – to understand your background and motivation.</span></span></li> <li><span><span>Live Coding Interview with the Hiring Team – a short technical task to evaluate your engineering and ML skills.</span></span></li> <li><span><span>Interview with the Hiring Manager – to discuss your experience, problem-solving approach, and team collaboration.</span></span></li> <li><span><span>Bar Raiser Interview with the Department Lead – to assess strategic alignment and long-term fit.<br/><br/>#EUR2<br/></span></span></li> </ul><p> </p> <p> </p>