Description
At Adyen, we're engineered for ambition. We empower our teams with the culture and support they need to own their careers.
As a Senior Machine Learning Engineer, you will join our team in Amsterdam and be responsible for designing, productionizing, and maintaining machine learning services that power data products at Adyen.
We have several teams currently hiring, including:
- Optimise: focused on maximizing transaction success, minimizing transaction costs, and driving payment performance at a global scale.
- Identity and Verification Decisioning: building the intelligence that determines how millions of businesses and individuals are verified throughout their lifecycle.
- Payments Core Data - Disputes: building ML and AI applications to make our dispute product more intelligent.
- AML Tech: building a modern, model-first detection engine to detect complex financial crime.
In this role, you will:
- Develop and maintain production ML pipelines for data ingestion, training, validation, and deployment.
- Identify and fix performance bottlenecks in ML training and inference.
- 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.
Who You Are:
- 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.
Nice to Have:
- Experience with distributed GPU compute environments.
- Experience working with a Machine Learning ‘Feature Store’.