Machine Learning Engineer (II and Senior)

Experteer Overview As a Machine Learning Engineer (II and Senior) at Adyen in Amsterdam, you will design, productionize and maintain scalable ML services powering data products. You’ll work with cross-functional teams to integrate ML into products and respond to evolving risk and optimization needs. You’ll improve training and inference performance and help turn research into deployable, robust systems. This role offers impact at scale within a fast-moving, collaborative environment. Pay / Benefits * Build and maintain production ML pipelines for data ingestion, training, validation and deployment * Tackle real-time and batch ML challenges across domains (risk, clustering, representation learning, AML, anomaly detection) * Identify and fix performance bottlenecks in training and inference * Collaborate with software engineers to integrate ML into products and services * Partner with data scientists to scale research prototypes and work with MLOps/platform teams to align tooling * Support and promote solid engineering practices within product ML teams Tasks * 5+ years of ML engineering experience * Strong Python programming * Experience with end-to-end ML lifecycle in production * Experience leveraging big data to feed models * Solid software engineering, data engineering and MLOps knowledge * Knowledge of data science, statistics and ML techniques * Hands-on experience with Python data science stack (pySpark, Trino SQL, TensorFlow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar, Airflow) * Familiarity with ML infrastructure (k8s, docker, airflow, argo-workflows, Prometheus, Grafana) * Experimental mindset and proactive project leadership * Effective communication with diverse stakeholders Key requirements *

Tasks and promote solid engineering practices within product ML teams Tasks * 5+ years of ML engineering experience * Strong Python programming * Experience with end-to-end ML lifecycle in production * Experience leveraging big data to feed models * Solid software engineering, data engineering and MLOps knowledge * Knowledge of data science, statistics and ML techniques * Hands-on experience with Python data science stack (pySpark, Trino SQL, TensorFlow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar, Airflow) * Familiarity with ML infrastructure (k8s, docker, airflow, argo-workflows, Prometheus, Grafana) * Experimental mindset and proactive project leadership * Effective communication with diverse stakeholders Key requirements *