Staff Machine Learning Engineer, Traffic Intelligence

What you'll do

• Own traffic-scoring models from problem framing through real-time deployment, managing adversarial feedback and reducing bot-incident MTTM.
• Build offline-to-online data pipelines and rigorous evaluation frameworks for reliable, measurable model performance.
• Optimize inference for millisecond edge latency and integrate scoring into automated mitigation workflows across regions.

What they ask for

• 9+ years of applied production ML experience in non-stationary, adversarial domains such as traffic integrity, bot mitigation, or fraud.
• Experience architecting scalable offline-to-online data pipelines for certified source-of-truth datasets and low-latency inference.
• Strong model evaluation skills, including ROC/AUC, precision/recall, and calibration; experience with warehouse-scale SQL and high-volume event-stream feature engineering.
• Practical knowledge of internet edge infrastructure, plus cross-functional technical leadership and mentoring experience.

Nice to have

• PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.
• Expertise in graph-based or Sybil detection, causal/econometric methods, Bayesian calibration, certified-data governance, or LLM agent tooling and benchmarking.

Pay

$212,000 — $265,000 USD (base salary)

Tools

SQL, ROC/AUC, CDN, HTTP/TLS

AI focus

Builds traffic-classification ML to distinguish legitimate AI assistants and other automated agents from abusive actors; preferred exposure includes LLM agent tooling and benchmarking.