Machine Learning Engineer

What you'll do

• Build and train machine learning models to identify financial fraud, device deception, and account abuse.
• Perform feature engineering on transaction, device, and behavioral event data.
• Develop and maintain feature pipelines using Databricks and Spark for high-quality model inputs.
• Translate experimental prototypes into scalable, reliable, and observable production ML systems.
• Implement MLOps practices including experiment tracking, model versioning, CI/CD, and monitoring.
• Analyze fraud and abuse patterns across various Adobe products and services to improve risk scores.
• Research and integrate advancements in machine learning for fraud detection and behavioral modeling.

What we're looking for

• Bachelor's degree in Computer Science, Machine Learning, Data Science, or a related field (or equivalent experience).
• 5+ years of professional experience building and deploying ML solutions, or equivalent experience through internships, research, or personal projects.
• Solid programming skills in Python with hands-on experience in PyTorch, TensorFlow, scikit-learn, or similar frameworks.
• Working understanding of the ML lifecycle from data collection through deployment and monitoring.
• Experience in payment fraud, device fingerprinting, account takeover detection, anomaly detection, or graph-based modeling (preferred).
• Exposure to sequence modeling, transformer architectures, or graph neural networks (preferred).
• Familiarity with Databricks, Spark, or large-scale transactional/event pipelines (preferred).