Staff Machine Learning Engineer, Traffic Intelligence

About the role

Job Description About Airbnb 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: Our web and API surfaces handle requests from guests and hosts alongside a growing volume of automated agents: AI assistants, crawlers, and scrapers. We build the systems that brin…

Requirements

• managed the feedback loop against adaptive actors.
• Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems.
• Strong foundation in rigorous model evaluation, including metrics like ROC/AUC, precision/recall, and calibration, with an ability to communicate complex trade-offs to cross-functional stakeholders.
• Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams to build reliable, production-ready modeling pipelines.
• Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures) and their role in verifying foundational signals.
• Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers on technical quality and design practices.
• MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience, with significant ownership of large-scale systems measuring evasion-resistance.
• PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.
• Advanced expertise in graph-based coordination or Sybil network detection methods for complex, distributed system analysis.
• Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic.
• Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets.
• Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts.

Where this was posted

• 4 Day Week

  last seen 2h ago
  View on 4 Day Week