Senior Machine Learning Engineer, Financial Products

Job description

This is Adyen. Adyen provides payments, data, and financial products in a single solution for customers like Meta, Uber, H&M, and Microsoft - making us the financial technology platform of choice. At Adyen, everything we do is engineered for ambition. For our teams, we create an environment with opportunities for our people to succeed, backed by the culture and support to ensure they are enabled to truly own their careers. We are motivated individuals who tackle unique technical challenges at scale and solve them as a team. Together, we deliver innovative and ethical solutions that help businesses achieve their ambitions faster. Financial Products About the Role The Financial Products org at Adyen is at the forefront of our evolution, building the foundational infrastructure that enables our customers to manage their finances, issue cards, and access credits and financing 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 the models, scorecards, and production systems that enable Adyen to scale its credit products 100x. As a Senior 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. In this role, you will: Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment. Examples ML domains are: supervised and semi-supervised learning methods for inference on credit risk patterns; 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; Who You Are: You have 5-7+ years of experience as an engineer working in the machine learning domain; You are a strong Python programmer and you have experience in Java. 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 You have an experimental mindset with a launch fast and iterate mentality; You proactively take the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences. Nice to Have: You have experience on underwriti