Staff Machine Learning Engineer, Traffic Intelligence

What you'll do

• Own the complete lifecycle of traffic-scoring models from problem framing to real-time deployment.
• Architect robust offline-to-online pipelines to produce certified source-of-truth datasets.
• Establish rigorous evaluation frameworks including stratified benchmarks and leakage-prevention checks.
• Execute model optimization to meet strict millisecond latency budgets at the internet edge.
• Integrate scoring intelligence into automated mitigation workflows with security and infrastructure partners.
• Communicate complex model trade-offs and technical research to leadership and cross-functional teams.
• Mentor junior engineers on technical quality, design practices, and production ML standards.

What we're looking for

• 9+ years of applied experience in production ML within non-stationary, adversarial domains like traffic integrity, bot mitigation, or fraud.
• Demonstrated experience architecting scalable, offline-to-online data pipelines for low-latency inference systems.
• Strong foundation in rigorous model evaluation metrics including ROC/AUC, precision/recall, and calibration.
• Experience with large-scale data engineering using warehouse-scale SQL and feature engineering on high-volume event streams.
• Practical knowledge of internet edge infrastructure, including CDN/load balancer behavior and HTTP/TLS signatures.
• Proven track record of cross-functional leadership and mentoring junior engineers on technical quality and design practices.
• MS/PhD in a quantitative field or equivalent deep engineering experience with ownership of large-scale systems.
• PhD in Statistics, Mathematics, or Machine Learning (preferred).