Machine Learning Engineer

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.

Key Responsibilities

• 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 inputs.
• Help translate prototypes into production ML systems, working with senior engineers on scalability, reliability, and observability.
• Support MLOps practices: experiment tracking, model versioning, CI/CD, and production monitoring.
• Collaborate cross-functionally with data science, product, and platform teams to understand fraud and abuse patterns across Adobe's surfaces.
• Stay ahead of advances in ML/AI, particularly in fraud detection and behavioral modeling, and bring relevant ideas to the team.

Minimum Qualifications

• 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.

Preferred Qualifications

• Coursework, projects, or professional experience in any of: payment fraud, device fingerprinting, account takeover detection, anomaly detection, or graph-based modeling.
• Exposure to sequence modeling, transformer architectures, or graph neural networks.
• Familiarity with Databricks, Spark, or large-scale transactional/event pipelines.

Expected Pay Range

Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $125,600 - $234,150 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. In California, the pay range for this position is $161,700 - $234,150

Application Window Notice

Oct 30 2026 12:00 AM

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.