Staff Machine Learning Engineer, Traffic Intelligence

Responsibilities

• Architect and maintain end-to-end traffic-classification machine-learning systems.
• Own the lifecycle of traffic-scoring models, from problem framing through real-time deployment.
• Manage adversarial feedback loops to improve evasion resistance and reduce bot-incident MTTM.
• Architect offline-to-online pipelines that produce certified source-of-truth datasets.
• Establish rigorous evaluation frameworks, including stratified benchmarks and leakage-prevention checks.
• Optimize models within millisecond latency budgets at the internet edge while balancing inference cost and incremental value.
• Maintain fleet-wide fail-open behaviors.
• Partner with security analysts, data platform engineers, and infrastructure partners to integrate scoring intelligence into automated mitigation workflows.
• Communicate model trade-offs to leadership and cross-functional teams.
• Mentor junior engineers on technical quality and design practices.

Requirements

• 9+ years of applied experience in production machine learning, particularly in non-stationary, adversarial domains such as traffic integrity, bot mitigation, or fraud.
• Experience architecting scalable offline-to-online data pipelines for low-latency inference systems.
• Strong foundation in model evaluation, including ROC/AUC, precision/recall, and calibration.
• Experience with large-scale data engineering, 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.
• Track record of cross-functional leadership using shared datasets and consumer contracts.
• MS/PhD in a quantitative field such as Statistics or Machine Learning, or equivalent deep engineering experience.

Nice-to-haves

• PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.
• Expertise in graph-based coordination or Sybil network detection.
• Experience with causal or econometric methods for modeling the business impact of false positives.
• Experience implementing Bayesian calibration for adversarially biased, sparse, or imbalanced datasets.
• Familiarity with data governance and platform engineering for certified datasets and downstream consumer contracts.
• Exposure to LLM agent tooling and benchmarking, including inference cost, latency, and value trade-offs.

Compensation and Benefits

• Base pay range: $212,000–$265,000 USD.
• May be eligible for bonus, equity, benefits, and Employee Travel Credits.