Senior Machine Learning Engineer, Supply & Competitive Intelligence

Airbnb

1d•$196,000 - $227,000•Remote

About The Position

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Marketplaces Data and AI is a group of passionate machine learning, software, data, and analytics engineers. We are responsible for developing new, cutting-edge AI and data products that leverage Airbnb’s massive datasets across Users, Listings, Pricing, and Supply/Demand. You will be a crucial part of the Guest and Host organization, building the intelligence that powers the web and native apps used by hundreds of millions of guests and hosts globally. The Difference You Will Make: Supply & Competitive Intelligence sits at the intersection of Data/AI and Airbnb's competitive strategy. As the engineer leading crawling infrastructure for the Competitive Intelligence platform, you will build the systems that give Airbnb a real-time, comprehensive view of the global travel market — how competitors price, list, and supply their inventory across every market Airbnb operates in. Your work will directly shape Airbnb's understanding of its competitive landscape, feeding ML models and data products that inform pricing strategy, supply growth, and product decisions for hundreds of millions of guests and hosts around the world.

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.

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