Staff ML Engineer - Remote Edge Traffic & Bot Detection

Airbnb; United States; On-site; USD 212,000 - 265,000; Full time

Job summary

Airbnb is seeking a senior ML leader to architect and maintain end-to-end traffic classification systems, balancing high-performance model deployment with robust offline data pipelines. You will harden edge-traffic policies and drive reductions in bot incidents for global platforms.

You will own the lifecycle of traffic-scoring models, build certified offline-to-online pipelines, and collaborate with security, data platforms, and international partners to ensure consistent, defensible ML

Qualifications

• 9+ years of production ML in adversarial domains with a feedback loop against adaptive actors.
• Experience architecting scalable offline-to-online data pipelines for low-latency inference.
• Strong model evaluation skills and ability to communicate trade-offs to stakeholders.

Responsibilities

• Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment and adversarial feedback loop management.
• Architect robust offline-to-online pipelines for certified source-of-truth datasets and rigorous evaluation frameworks.
• Execute model optimization within millisecond latency budgets at the internet edge, balancing inference cost and value while maintaining fail-open behavior.
• Partner with security analysts, data platform engineers, and international infrastructure teams to integrate scoring into automated mitigation workflows for global consistency.
• Serve as ML authority, translating research into practical, scalable engineering guidance for leadership and teams.

Skills

Applied ML in adversarial domains

Data pipelines offline-to-online

Model evaluation ROC AUC

Large-scale data engineering

Edge infrastructure knowledge

Cross-functional leadership

Quantitative degree or equivalent

Education

MS/PhD in Statistics/ML or equivalent

Tools

SQL

Job description

Airbnb is seeking a senior ML leader to architect and maintain end-to-end traffic classification systems, balancing high-performance model deployment with robust offline data pipelines. You will harden edge-traffic policies and drive reductions in bot incidents for global platforms.

You will own the lifecycle of traffic-scoring models, build certified offline-to-online pipelines, and collaborate with security, data platforms, and international partners to ensure consistent, defensible ML