This role focuses on building and scaling the feature ingestion and storage infrastructure that powers both core business logic and ML applications at Whatnot. The engineer will work with ML scientists to leverage near-real-time signals for critical business surfaces like growth, recommendations, trust and safety, and fraud. Key responsibilities include designing and evolving real-time feature pipelines for online and offline stores, optimizing system performance, and empowering scientists with abstractions and tools for near-real-time features. The role requires experience with production ML systems, software engineering for consumer-scale loads, Python, and various databases and cloud platforms.