Adobe is changing the world through digital experiences. We help our customers create, deliver and optimize content and applications.
About the role
Adobe is seeking a Machine Learning Engineer to join the Adobe Risk Platform (ARP) team. ARP is Adobe's centralized, adaptive system for detecting, preventing, and mitigating fraud and abuse across products and services — protecting surfaces like Commerce, Stock, and Firefly with real-time risk decisions, without degrading the customer experience.
What they're looking for
- Bachelor's degree in Computer Science, Machine Learning, Data Science, or related field (or equivalent experience)
- 3+ 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
- Eagerness to learn model optimization, inference efficiency, and production system integration, with support from senior engineers on the team
More about this role
Adobe is seeking a Machine Learning Engineer to join the Adobe Risk Platform (ARP) team. ARP is Adobe's centralized, adaptive system for detecting, preventing, and mitigating fraud and abuse across products and services — protecting surfaces like Commerce, Stock, and Firefly with real-time risk decisions, without degrading the customer experience.
In this position, you will help build and develop machine learning models to identify fraudulent activity, detect abusive account behavior, and protect the experience of hundreds of millions of users. You'll work across the model lifecycle from raw behavioral data and feature engineering, to model training, deployment, and monitoring alongside senior engineers on the team.
- Help build and train ML models covering various fraud and abuse areas. These include financial transaction fraud, device-related deception, and account and identity abuse. The goal is a unified, continuously-updated trust and risk score.
- Contribute to feature engineering across transaction, device, and behavioral event data.
- Build and maintain feature pipelines on Databricks and Spark, transforming raw transaction and device event data into high-quality model...
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