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Adyen is seeking a Senior Machine Learning Engineer to join their Amsterdam office and design, productionize, and maintain machine learning services that power data products across the organization.
In this role, you will develop and maintain production ML pipelines for data ingestion, training, validation, and deployment across multiple domains including online learning algorithms, clustering, supervised/semi-supervised learning for risk pattern inference, representation learning for behavior prediction, Anti-Money Laundering (AML) systems, and real-time anomaly detection based on time-series modeling. You'll identify and fix performance bottlenecks in ML training and inference (memory consumption, latency, training time), collaborate with software engineers to integrate ML solutions into products and services, work with data scientists to transition research prototypes into scalable solutions, and partner with MLOps and platform teams to integrate with current tools and shape priorities for future infrastructure.
You'll support and encourage good engineering practices across product ML teams and take a proactive leadership role in projects from ideation through deployment, communicating complex outcomes to diverse stakeholders.
Required qualifications include 5+ years as an ML engineer, 5+ years with general-purpose programming languages (Java, C/C++, Python), 5+ years in software development, 5+ years testing and launching software products with 3+ years in software design and architecture, and 5+ years building and deploying ML systems in production across prediction, ranking, embedding, and deep learning domains. You need strong experience with ML design and infrastructure (model deployment, evaluation, data processing, debugging, fine-tuning), big data pipeline creation, software engineering practices, data engineering, and MLOps principles. Proficiency with the Python data science toolkit (PySpark, Trino SQL, TensorFlow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow, Airflow) and ML infrastructure tools (Kubernetes, Docker, Airflow, Argo Workflows, Prometheus, Grafana) is essential. An experimental mindset with launch-fast-iterate mentality is valued.
Nice-to-have skills include experience with distributed GPU compute environments and machine learning feature stores. Multiple teams are currently hiring including Optimise, Protect, Identity, Behavioural Fraud, and Regulatory Reporting Technology.
