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

We are looking for a

(Senior) Machine Learning Engineer (m/f/d)
Full-time at our location in Berlin or Amsterdam - hybrid working conditions available.

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.

Your Responsibilities:

• Design, build, and maintain end-to-end machine learning pipelines — from data ingestion and preprocessing to model deployment and monitoring.
• Develop scalable and robust machine learning solutions that power risk and fraud decisioning.
• Collaborate with data scientists to turn experimental models into efficient production-ready systems.
• Improve and optimize existing models, infrastructure, and workflows for reliability, performance, and maintainability.
• Conduct code reviews and contribute to our ML engineering best practices.
• Work closely with product managers, data engineers, and cross-functional teams to integrate ML models into customer-facing applications.

What You Bring:

• Solid experience as a Machine Learning Engineer, MLOps Engineer, or similar role with a focus on deploying ML models at scale.
• Strong software engineering skills in Python, especially in the context of building and deploying ML solutions.
• Hands-on experience with Kubernetes for container orchestration and scalable ML deployments.
• Experience deploying models to production using frameworks such as MLflow, FastAPI, or similar.
• Familiarity with model monitoring, retraining strategies, and performance evaluation in a production setting.
• Proficiency with version control, CI/CD pipelines, and containerization tools like Docker.
• Understanding of working with imperfect, real-world datasets collected outside of typical ML workflows.
• A degree in Computer Science, Machine Learning, Engineering, or a related STEM field.Experience working with transactional databases or case handling systems.

Bonus Skills:

• Experience with tools such as SQL, Spark, Databricks, VS Code, and Docker.
• Familiarity with cloud infrastructure (e.g., AWS, Azure, or GCP) for deploying machine learning systems.
• Prior exposure to the risk, fraud, or fintech domain.
• Knowledge of MLOps practices and tools for model lifecycle management.

Our Hiring Process

We aim to make our hiring process smooth and transparent:

• Pre-screening Call with HR – to understand your background and motivation.
• Live Coding Interview with the Hiring Team – a short technical task to evaluate your engineering and ML skills.
• Interview with the Hiring Manager – to discuss your experience, problem-solving approach, and team collaboration.
• Bar Raiser Interview with the Department Lead – to assess strategic alignment and long-term fit.
  
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