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.
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.
ML system evaluation and metrics
Design comprehensive evaluation strategies including offline metrics (precision, recall, F1, AUC, calibration), online metrics (A/B test setup, statistical significance), and business metrics. Understand metric limitations and how to avoid gaming metrics.
Airbnb AI/ML Applications and Product Vision
Airbnb-specific discussion of how AI/ML capabilities are developed and applied across Airbnb's product portfolio, including practical deployment considerations, ML architectures, experimentation, product strategy, and governance for ML-enabled features (search, pricing, recommendations, image recognition, fraud detection, and user experience improvements). Emphasizes real-world machine learning systems in production and alignment with product strategy.
On-Device ML for Apple Platforms
Techniques and considerations for running machine learning models directly on devices (edge inference) on Apple platforms such as iPhone, iPad, and Vision Pro. Topics include Core ML integration, model optimization (quantization, pruning), on-device privacy and offline capabilities, performance tuning, and deployment strategies for mobile and AR devices.