Senior Machine Learning Engineer, Supply & Competitive Intelligence

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.