Conduct experiments on state-of-the-art machine learning models for real-world, large-scale problems, including model architecture design, feature engineering, and performance optimization.
Enhance the infrastructure that powers large-scale ML experimentation, training, and serving. This includes TPU/GPU-based model training, microservices for ML inference, scalable data pipelines (Spark / Apache Beam), and systems for ML data validation and monitoring.