- Build and improve recommendation and search systems across feed, discovery, search, and content continuation surfaces.
- Own retrieval and ranking systems, including candidate generation, embedding-based retrieval, two-tower models, ranking features, and online serving quality.
- Design, launch, and analyze recommendation and search experiments end-to-end, then use the data to iterate.
- Improve recommendation quality for new users, new content, and fast-changing content pools.
- Build user, content, creator, and session-level representations from behavioral signals.
- Partner with product, data, and engineering teams to define metrics, run experiments, and ship improvements to retention, engagement, and content distribution.
- Build practical ML systems that move from prototype to production, with monitoring and evaluation.
- Help shape the long-term ML architecture for AI-native content discovery.
Machine Learning