The opportunity
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…
What you'll do
Own the complete lifecycle of traffic-scoring models, from problem framing to: real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM.
Architect robust offline-to-online pipelines that produce certified: source-of-truth datasets, establishing rigorous evaluation frameworks—such as stratified benchmarks and leakage-prevention checks—to ensure every model improvement is empirically measurable and defensible.
Execute model optimization within strict millisecond latency budgets at the: internet edge, uniquely balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors.
Partner daily with security analysts, data platform engineers, and: international infrastructure partners to integrate scoring intelligence into automated mitigation workflows, ensuring global consistency in traffic classification despite regional failovers or CDN updates.
Serve as the team’s machine learning authority, communicating complex model: trade-offs to leadership and cross-functional teams to translate technical research into practical, scalable engineering guidance.
+ years of applied experience in production ML, specifically within: non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud) where you have managed the feedback loop against adaptive actors.
What they're looking for
- 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.
