Machine Learning Engineer

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, and downstream fine-tuning. Train large models efficiently on GPU infrastructure using mixed-precision training, gradient accumulation, and distributed strategies. 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. Know the latest advances in ML/AI and bring relevant innovations into Adobe's products.