Staff Machine Learning Engineer, Traffic Intelligence
Core
Architect and maintain end-to-end traffic classification ML systems to distinguish legitimate automation from abusive actors in real-time, hardening edge-traffic policies and ensuring signal accuracy across billions of daily requests.
Role type
Staff Machine Learning Engineer (Traffic Intelligence)
Builds
Real-time traffic scoring models, offline-to-online data pipelines, and evaluation frameworks for bot mitigation and anti-scraping detection.
Domain
Internet infrastructure, traffic integrity, adversarial ML, bot mitigation
Deliverable
production ML models
Required skills
Production ML in adversarial domains, offline-to-online pipeline architecture, rigorous model evaluation (ROC/AUC, calibration), large-scale data engineering, internet edge infrastructure knowledge, cross-functional leadership, mentoring
Preferred skills
PhD in Statistics/ML, graph-based coordination/Sybil detection, causal/econometric modeling, Bayesian calibration, LLM agent tooling, data governance
Technologies
SQL, CDN/load balancer, HTTP/TLS signatures, Bayesian methods, graph algorithms
Responsibilities
Own the complete lifecycle of traffic-scoring models from framing to deployment; Architect robust offline-to-online pipelines producing certified datasets; Execute model optimization within strict millisecond latency budgets; Partner with security and infrastructure teams to integrate scoring intelligence; Serve as ML authority communicating trade-offs to leadership and cross-functional teams
Seniority
Staff, hands-on IC with strategic influence
