Staff ML Engineer — Credit Risk & Production Pipelines

Fdetimes; San Francisco (CA); On-site; USD 297,000 - 401,000; Full time

Job summary

Adyen is building a San Francisco-based Machine Learning Engineering team focused on credit risk modeling for underwriting within Financial Products. As a Staff ML Engineer, you will design, productionize, and operate ML models and rule-based decision systems across the full model lifecycle.

You will work with product, engineering, risk, and data teams to deliver underwriting capabilities for leading clients while balancing speed, robustness, explainability, and global scale.

Qualifications

• 8+ years of engineering experience in machine learning
• Strong Python and Java proficiency
• Experience with end-to-end ML model lifecycle in production
• Experience processing large datasets to feed ML models
• Good software engineering and data engineering practices with MLOps familiarity

Responsibilities

• Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment
• Identify and fix performance bottlenecks in training and inference
• Collaborate with software engineers to integrate ML solutions into products and services
• Collaborate with data teams to align with current tooling and future priorities
• Promote good engineering practices on product ML teams

Skills

Python

Java

ML lifecycle

Big data pipelines

MLOps

Data science

Communication

Leadership

Tools

k8s

Docker

Airflow

TensorFlow

PyTorch

XGBoost/LightGBM

Pandas

Spark

SQL

Job description

Adyen is building a San Francisco-based Machine Learning Engineering team focused on credit risk modeling for underwriting within Financial Products. As a Staff ML Engineer, you will design, productionize, and operate ML models and rule-based decision systems across the full model lifecycle.

You will work with product, engineering, risk, and data teams to deliver underwriting capabilities for leading clients while balancing speed, robustness, explainability, and global scale.