Staff Machine Learning Engineer, Financial Products

Adyen•San Francisco, CA

6d•Onsite

About The Position

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 the models, scorecards, and production systems that enable Adyen to scale its credit products 100x. 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 is ideal for an engineer who 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. We are looking for engineers with a customer-problem-first mindset and experience building reliable ML systems in production. You will work closely with product, engineering, risk, and data teams to deliver underwriting capabilities for some of the world’s leading businesses.

Requirements

8+ years of experience as an engineer working in the machine learning domain.
Strong Python programmer and experience in Java.
Experience with the full machine learning model lifecycle in production flows.
Experience leveraging big data to create the pipelines needed to feed the models with appropriate data.
Strong understanding of good software engineering practices as well as data engineering and MLOps principles.
Knowledge of data science, statistics and machine learning techniques.
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.
Knowledge/experience of working with ML infrastructure components with tools such as k8s, docker, airflow, argo-workflows, prometheus, grafana.
An experimental mindset with a launch fast and iterate mentality.
Proactively take the lead in projects, from ideation to deployment.
Experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences.

Nice To Haves

Experience on underwriting models or systems.
Experience working with a Machine Learning ‘Feature Store’.

Responsibilities

Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment.
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 CreditOps and data teams to integrate effectively with current tools, and shape priority for future tools.
Support and encourage good engineering practices on product ML teams.