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Salary: USD 297,000 - 401,000 / annual
Adyen is building a Machine Learning Engineering team in San Francisco focused on Credit Risk Modeling for Underwriting within its Financial Products organization. The Financial Products org is at the forefront of Adyen's evolution, building foundational infrastructure that enables customers to manage finances, issue cards, and access credits and financing globally.
As a Staff Machine Learning Engineer, you will design, productionize, and operate machine learning models and rule-based decision systems that power credit products. You will work across the full model lifecycle, from research and data analysis to training, deployment, monitoring, and continuous improvement. This role combines strong machine learning and production engineering experience with sound judgment in high-integrity financial systems. You will help build continuous data flywheels that improve underwriting decisions while balancing rapid product innovation with robustness, explainability, and global scale.
Key responsibilities include:
- Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment, including supervised and semi-supervised learning methods for credit risk pattern inference
- Identify and fix performance bottlenecks in ML training and inference (memory consumption, online latency, training time)
- Collaborate with software engineers to integrate ML solutions into products and services
- Work with CreditOps and data teams to integrate with current tools and shape priorities for future tools
- Support and encourage good engineering practices on product ML teams
- Proactively lead projects from ideation to deployment, working with diverse stakeholders and communicating complex outcomes across audiences
You will have direct access to massive global datasets (payments, identity data) and see your team's work have immediate impact at scale. The role offers an environment of ownership and speed where a focused team can make significant difference at the intersection of ML and fintech.
Requirements:
- 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 that feed models with appropriate data
- 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 in Python: (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
Nice to have:
- Experience with underwriting models or systems
- Experience working with a Machine Learning Feature Store
