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 fine-tuning.
Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation/checkpointing, efficient attention, and distributed strategies (DDP, FSDP, or equivalent).
Build and optimize feature pipelines on Databricks and Spark, transforming raw behavioral events into high-quality model inputs.
Translate prototypes into production ML systems — scalable, reliable, and observable — and drive inference performance through architectural and serving-side optimization.
Contribute to MLOps practices: experiment tracking, model versioning, CI/CD, automated retraining, and production monitoring.
Collaborate cross-functionally with data science, product, and platform teams; mentor junior engineers on experimentation rigor, deployment process, and responsible AI.
Stay current with advances in ML/AI and bring relevant innovations into Adobe's products.
Bachelor’s degree or Master’s degree or equivalent experience in Computer Science, Machine Learning, Data Science, or related field.
5+ 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.
About Adobe
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