Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Personalization and Ranking Systems
Designing personalization and ranking architectures that operate at very large scale. Candidates should cover candidate generation and ranking pipelines, offline and real time feature engineering, feature stores, model training and serving, learning to rank approaches, latency and freshness tradeoffs, using in memory structures such as prefix tries for fast type ahead, experimentation and A B testing infrastructure, online evaluation and feedback loops, and data privacy and governance concerns.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.