Multi-entity recommendations across the partnership graph
Design, build, and evaluate recommendation models that operate across heterogeneous entities—advertisers, publishers, creators, products, and consumers—and the relationships between them. Frame problems in terms of the partnership graph and apply techniques appropriate to each surface, including candidate generation, ranking, reranking, and personalization.
Graph-based modeling & semantic embeddings
Contribute to evolving our architecture toward graph-based approaches: learn semantic embeddings of entities and relationships, apply graph neural networks or attention aware graph transformer models where they add value, and build representations that generalize across surfaces and use cases. Stay current with cutting-edge techniques in graph ML, representation learning, and modern recommender architectures, and bring relevant ideas into the platform.
Batch and real-time serving
Build models and pipelines that serve recommendations in both batch and real-time contexts. Partner with Engineering on retrieval infrastructure, vector search, feature stores, and low-latency serving patterns. Make pragmatic tradeoffs between model sophistication, latency, cost, and freshness based on the surface and use case.
End-to-end ML delivery & ML engineering
Own the full lifecycle of your work: data and feature design, model development, evaluation, launch, monitoring, and iteration. Build production-grade pipelines, write code that other engineers can extend, and partner with MLOps on reproducibility, observability, and reliability. Use AI coding agents aggressively to accelerate prototyping, refactoring, debugging, and shipping—we expect this to be a core part of how you work, not an occasional aid.
Experimentation & measurement
Design offline evaluation (offline replay, counterfactual evaluation, holdout sets) and online experiments (A/B tests, holdouts, interleaving) to quantify model impact. Apply appropriate statistical methods, recognize common pitfalls in recommender evaluation (position bias, feedback loops, selection effects), and translate results into clear recommendations for product and engineering partners.
Cross-functional collaboration
Work closely with Product, Engineering, and Business Stakeholders to translate platform goals into measurable model outcomes. Communicate findings, tradeoffs, and recommendations clearly to both technical and non-technical audiences. Document your work so that models, features, and decisions are understandable and reproducible by others.