Senior Machine Learning Engineer, Supply & Competitive Intelligence

Airbnb is a global platform that connects hosts with guests for unique stays and experiences. They are seeking a Senior Machine Learning Engineer to lead the development of the Competitive Intelligence platform, focusing on building systems that provide real-time insights into the global travel market.

Responsibilities:

• Design and deploy ML models that extract and structure competitive intelligence signals — supply availability, pricing patterns, and market saturation — from large-scale crawled datasets across global competitors
• Build and maintain end-to-end ML pipelines spanning feature engineering, offline training, and low-latency online serving, ensuring high data fidelity and resilience to upstream schema drift
• Apply entity resolution and matching techniques to accurately map competitor listings and markets to Airbnb's internal supply taxonomy, using methods such as embedding models, gradient boosted trees, and transformer-based architectures
• Partner with the crawling infrastructure engineer, data engineers, and product teams to translate competitive intelligence needs into well-defined ML problem formulations and measurable success criteria
• Run rigorous offline and online experiments to evaluate model quality, and collaborate with Pricing, Supply Growth, and Strategy stakeholders to turn model outputs into actionable business decisions
• Stay current with the latest advances in ML and AI, identifying opportunities to incorporate new techniques into the competitive intelligence platform

Requirements:

• 5–10 years of professional experience in applied Machine Learning, with a proven track record of architecting and deploying high-impact models into production at global scale
• Exceptional programming proficiency in Python (required), with additional experience in Scala, Java, or similar languages for building robust backend systems
• Deep mastery of ML fundamentals and best practices—including feature engineering, model selection, A/B testing, and training/serving skew mitigation—alongside advanced algorithms like gradient boosted trees, neural networks, and transformers
• Hands-on expertise with modern ML frameworks and tooling, such as TensorFlow or PyTorch, to drive innovation in model development
• Experience leading data engineering efforts to build end-to-end ML pipelines, encompassing both high-throughput batch processes and low-latency real-time systems
• Strong command of architectural patterns for high-scale software applications, including the design of extensible APIs, efficient algorithms, and resilient data infrastructure
• A disciplined approach to software craft, including test-driven development, incremental delivery, and modern CI/CD deployment practices
• A Bachelor's, Master's, or PhD in Computer Science, Machine Learning, or a closely related technical field