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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 at scale. This is a full-time, in-office role requiring relocation if you are not currently in the Netherlands.
You will work on one of several specialized teams:
**Optimize Intelligence**: A team focused on maximizing transaction success and minimizing costs through advanced ML/DL solutions. You'll build systems that evaluate high-dimensional data streams to dynamically select optimal authentication methods, apply payment message optimizations, and orchestrate intelligent transaction routing. These models live-score millions of daily transactions, balancing coupled cost and performance objectives in real time.
**Identity and Verification Decisioning**: Help define how Adyen makes identity decisions at global scale. This newly formed engineering team sits at the intersection of machine learning, distributed systems, fraud prevention, compliance, and customer experience. You'll have significant influence over technical direction, architecture, and ML strategy from an early stage, directly impacting fraud prevention, regulatory compliance, and customer onboarding.
**Payments Core Data - Disputes**: Build ML and AI applications to make Adyen's dispute product more intelligent. This is a greenfield opportunity to build disputes machine learning and AI use cases from the ground up, working with data engineers, ML scientists, and the disputes engineering team.
**AML Tech**: Process massive transaction volume to detect complex financial crime. Adyen is transitioning from a hybrid stack of business rules and ML models to a modern, model-first detection engine. You'll architect scalable batch processing pipelines, tackle challenges like extreme class imbalance and incomplete ground truth, and drive production ML systems.
**Key Responsibilities:**
- Develop and maintain production ML pipelines for data ingestion, training, validation, and deployment (online learning, clustering, supervised/semi-supervised learning, representation learning, anomaly detection, AML systems)
- Identify and fix performance bottlenecks in ML training and inference (memory, latency, training time)
- Collaborate with software engineers to integrate ML solutions into products and services
- Transition research prototypes into scalable solutions with data scientists
- Work with MLOps and platform teams to integrate with current tools and shape future priorities
- Support and encourage good engineering practices on product ML teams
**Requirements:**
- 5+ years of experience as an engineer in the machine learning domain
- Strong Python programming skills
- Experience with the full machine learning model lifecycle in production flows
- Experience leveraging big data to create pipelines for model feeding
- Strong understanding of software engineering practices, data engineering, and MLOps principles
- Knowledge of data science, statistics, and machine learning techniques
- Strong familiarity with standard data science toolkit: (py)spark, (Trino) SQL, TensorFlow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar MLOps frameworks, and Airflow
- Knowledge/experience with ML infrastructure components: Kubernetes, Docker, Airflow, Argo Workflows, Prometheus, Grafana
- Experimental mindset with launch-fast-and-iterate mentality
- Ability to proactively lead projects from ideation to deployment and communicate complex outcomes to diverse stakeholders
**Nice to Have:**
- Experience with distributed GPU compute environments
- Experience working with a Machine Learning Feature Store
