Staff Machine Learning Engineer, Traffic Intelligence

About the Role

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 bring clarity to this traffic, combining in-house ML and vendor signals to decide in real time how to serve billions of daily requests. You will architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines.

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 model improvements are empirically measurable.
• Execute model optimization within strict millisecond latency budgets at the internet edge, balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors.
• Partner with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows.
• 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 engineering guidance.

Qualifications

Minimum Qualifications

• 9+ years of applied experience in production ML, specifically within non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud).
• Demonstrated experience architecting scalable, offline-to-online data pipelines producing certified source-of-truth datasets for low-latency inference systems.
• Strong foundation in rigorous model evaluation (ROC/AUC, precision/recall, calibration) with the ability to communicate trade-offs to cross-functional stakeholders.
• Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams.
• Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures).
• Proven track record of cross-functional leadership and mentoring junior engineers.
• MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience.

Preferred Qualifications

• 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.
• Exposure to LLM agent tooling and benchmarking, optimizing inference costs against latency and value.