Senior Machine Learning Engineer

Responsibilities

• Develop and maintain scalable production ML pipelines for feature engineering, model training, validation, and deployment.
• Build and operate machine learning models and rule-based decision systems for credit products and underwriting.
• Identify and resolve performance bottlenecks involving memory consumption, online latency, and training time.
• Collaborate with software engineers to integrate ML solutions into products and services.
• Partner with CreditOps and data teams to integrate current tools and shape future tooling priorities.
• Support strong engineering practices across product ML teams.
• Work across the full model lifecycle, from research and data analysis through deployment, monitoring, and continuous improvement.

Requirements

• Require 5–7+ years of experience as an engineer working in machine learning.
• Strong Python programming skills and experience with Java.
• Experience with the full machine learning model lifecycle in production.
• Experience using big data to build pipelines that provide appropriate model inputs.
• Strong understanding of software engineering practices, data engineering, and MLOps principles.
• Knowledge of data science, statistics, and machine learning techniques.
• Familiarity with Python data science tools including PySpark, Trino SQL, TensorFlow, PyTorch, XGBoost or LightGBM, Pandas, MLflow or similar MLOps frameworks, and Airflow.
• Experience with ML infrastructure tools such as Kubernetes, Docker, Airflow, Argo Workflows, Prometheus, and Grafana.
• Ability to lead projects from ideation through deployment and communicate complex outcomes to varied stakeholders.
• Experience with underwriting models or systems is preferred.
• Experience working with a machine learning feature store is preferred.

Benefits

• Annual base salary range of $227,500–$317,000 plus RSUs.
• Based in the San Francisco office; this is an office-first role and not remote-only.
• Potential future relocation discussions for Adyen offices in Amsterdam or Madrid.
• Access to large global payments and identity datasets and the opportunity to work on ML and fintech problems at scale.
• Small teams, direct communication, ownership, and a startup-oriented environment within an established company.
• Adyen provides accommodations and recruitment-process support for candidates who need them.