Bree

• Design, develop, and deploy end-to-end machine learning pipelines, ensuring efficiency in training, validation, and inference. • Implement MLOps best practices, including CI/CD for ML models, model versioning, monitoring, and retraining strategies. • Optimize ML models using feature engineering, hyperparameter tuning, and scalable inference techniques. • Work with structured and unstructured data, leveraging Pandas, NumPy, and SQL for efficient data manipulation. • Apply machine learning design patterns to build modular, reusable, and production-ready models. • Collaborate with data engineers to develop high-performance data pipelines for training and inference. • Deploy and manage models on cloud platforms (AWS, GCP, Azure) with containerization and orchestration tools like Docker and Kubernetes. • Maintain model performance by implementing continuous monitoring, bias detection, and explainability techniques.

• Own the weekly creative pipeline end-to-end. • Work closely with our Media Buyer to turn performance data into creative direction. • Build and scale our creator engine. • Design and refine the systems that power the creative team. • Set the direction for what we test and why. • Build tight feedback loops between performance and production.