Staff Machine Learning Engineer, Traffic Intelligence
About the role
Architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines to harden edge-traffic policies and ensure accurate, evasion-resistant traffic classification across the fleet.
Responsibilities
- 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.
Qualifications
- 9+ 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.
- 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.
Skills
Benefits
- Pay range: $212,000—$265,000 USD
- Bonus, equity, benefits, and Employee Travel Credits may apply
About Airbnb
Book accommodations around the world.
Founded in August of 2008 and based in San Francisco, California, Airbnb is a trusted community marketplace for people to list, discover, and book unique accommodations around the world — online or from a mobile phone. Whether an apartment for a night, a castle for a week, or a villa for a month, Airbnb connects people to unique travel experiences, at any price point, in more than 33,000 cities and 192 countries. And with world-class customer service and a growing community of users, Airbnb is the easiest way for people to monetize their extra space and showcase it to an audience of millions. No global movement springs from individuals. It takes an entire team united behind something big. Together, we work hard, we laugh a lot, we brainstorm nonstop, we use hundreds of Post-Its a week, and we give the best high-fives in town. Headquartered in San Francisco, we have satellite offices in Dublin, London, Barcelona, Paris, Milan, Copenhagen, Berlin, Moscow, São Paolo, Sydney, and Singapore.
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