Staff Machine Learning Engineer, Financial Products

About the Role

The Financial Products org at Adyen is building foundational infrastructure enabling customers to manage finances, issue cards, and access credit globally. Adyen is building a Machine Learning Engineering team in San Francisco focused on Credit Risk Modeling for Underwriting within Financial Products. This team will develop models, scorecards, and production systems to scale credit products 100x.

As a Staff Machine Learning Engineer, you will design, productionize, and operate ML models and rule-based decision systems powering credit products across the full model lifecycle—from research and data analysis to training, deployment, monitoring, and continuous improvement.

Responsibilities

• Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment (e.g., supervised and semi-supervised learning methods for credit risk inference).
• Identify and resolve 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 CreditOps and data teams to integrate effectively with current tools and shape priorities for future tools.
• Support and encourage good engineering practices on product ML teams.

Qualifications

Required

• 8+ years of experience as an engineer working in the machine learning domain.
• Strong Python programming skills and experience with Java.
• Experience with the full machine learning model lifecycle in production flows.
• Experience leveraging big data to create pipelines needed to feed models.
• Strong understanding of software engineering practices, data engineering, and MLOps principles.
• Knowledge of data science, statistics, and machine learning techniques.
• Strong familiarity with the standard data science toolkit in Python (e.g., PySpark, Trino/SQL, TensorFlow, PyTorch, XGBoost/LightGBM, Pandas, MLflow or similar MLOps frameworks, Airflow).
• Knowledge/experience working with ML infrastructure components (e.g., Kubernetes, Docker, Airflow, Argo Workflows, Prometheus, Grafana).
• Experimental mindset with a launch fast and iterate mentality.
• Ability to proactively lead projects from ideation to deployment, with strong communication across diverse stakeholder groups.

Preferred

• Experience with underwriting models or systems.
• Experience working with a Machine Learning Feature Store.

Hiring Process

Adyen aims to respond to applications within 5 business days. The standard interview process takes about 4 weeks to complete.