Staff Machine Learning Engineer (Traffic Intelligence)
Confirmed on the employer's own hiring board on Oct 13, 2026. First seen by Alion on Aug 18, 2026. Airbnb scores B on the Alion truth index.
Join Airbnb as a Staff Machine Learning Engineer specializing in Traffic Intelligence. In this role, you will architect and maintain end-to-end traffic classification ML systems, manage the complete lifecycle of traffic-scoring models, and execute model optimization within strict latency budgets. You will collaborate with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows. Additionally, you will serve as the team's machine learning authority, communicating complex model trade-offs to leadership and cross-functional teams.
Missions
- Architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines.
- 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.
- Partner daily with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows.
Profil recherché
- 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- Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems
- 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
- 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
- Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures) and their role in verifying foundational signals
- 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
- Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams to build reliable, production-ready modeling pipelines
- 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
- Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts
- Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets
- Exposure to LLM agent tooling and benchmarking, with a focus on optimizing inference costs against latency and value trade-offs