Machine learning engineer in payments

Adyen is looking for a Senior Machine Learning Engineer to join our team in Amsterdam, someone with experience of building and operating robust machine learning systems at scale in production environments. You will be responsible for designing, productionizing and maintaining machine learning services that power data products at Adyen.

• Develop and maintain production ML pipelines for data ingestion, training, validation, and deployment. Examples ML domains are- on-line learning algorithms to pick the best optimization decision in a changing environment, clustering algorithms to group customers/shoppers, supervised and semi-supervised learning methods for inference on risk patterns or graph analysis, representation learning for behavior prediction and monitoring, Anti-Money Laundering (AML) systems and real-time anomaly detection based on time-series modeling;
• Identify and fix performance bottlenecks in ML training and inference (memory consumption, online latency, training time etc.);
• Collaborate with software engineers to integrate ML solutions into products and services;
• Collaborate with data scientists to transition research prototypes into scalable solutions;
• Collaborate with MLOps and platform teams to integrate effectively with current tools, and shape priority for future tools;
• Support and encourage good engineering practices on product ML teams;

• You have 5+ years of experience as an engineer working in the machine learning domain;
• You are a strong Python programmer;
• You have experience with the full machine learning model lifecycle in production flows;
• You have experience leveraging big data to create the pipelines needed to feed the models with appropriate data;
• You have a strong understanding of good software engineering practices as well as data engineering and MLOps principles;
• You have knowledge of data science, statistics and machine learning techniques;
• You have strong familiarity with the standard data science toolkit in python, such as (py)spark, (Trino) SQL, Tensorflow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar MLOps frameworks, and Airflow;
• You have knowledge/experience of working with ML infrastructure components with tools such as k8s, docker, airflow, argo-workflows, prometheus, grafana
• You have an experimental mindset with a launch fast and iterate mentality;
• You proactively take the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences.
• Будет плюсом: You have experience with distributed GPU compute environments; You have experience working with a Machine Learning ‘Feature Store’

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