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 Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide.
What they're looking for
- Bachelor’s degree or Master’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field
- 8+ years of professional experience building and deploying ML solutions at scale
- Strong programming expertise in Python, with hands-on experience in PyTorch, TensorFlow, or similar frameworks
- Deep understanding of the end-to-end ML lifecycle—from data collection to deployment and monitoring
- Strong grasp of model optimization, inference efficiency, and production system integration
- Experience in fraud detection, anomaly detection, or behavioral modeling
More about this role
Adobe is seeking a Machine Learning Engineer to join the Adobe Genuine Engineering team. This group protects Adobe's ecosystem from fraud, abuse, and misuse using intelligent systems worldwide.
In this position, you will build and develop machine learning models from scratch, including custom transformer-based frameworks, to identify fraudulent actions, stop account sharing, and protect the experience of hundreds of millions of users. You will manage the entire model lifecycle: raw behavioral data and feature engineering, architecture development, large-scale GPU training, deployment, and monitoring.
The team is actively building in-house behavioral foundation models that learn identity-preserving representations from long sequences of user activity. This is a role for an engineer who wants to own deep learning systems end-to-end — not consume pre-built ones.
Build and train deep learning models from scratch, including custom transformer and attention-based architectures for long behavioral event sequences.
Own the full training stack: event tokenization, temporal and positional embeddings, self-supervised pretraining (e.g., masked modeling, contrastive learning), and downstream...
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