Staff Machine Learning Engineer, Traffic Intelligence

Airbnb
United States
Workplace: RemoteFull timeUSD 212,000 - 265,000 annuallyFunction: Data Science & Machine LearningExperience: 9+ yearsEducation: mastersSkills: ["Cross-functional leadership","Mentoring","Technical communication","Evaluation rigor"]

Architect and maintain end-to-end traffic classification ML systems that distinguish legitimate automation from abusive actors. Build and harden offline-to-online pipelines producing certified source-of-truth datasets, and run rigorous evaluation frameworks (benchmarks and leakage checks). Deploy models with millisecond latency budgets at the internet edge, managing adversarial feedback loops and partnering with security and infrastructure teams to drive down bot-incident MTTM across the fleet.

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FursaFursa
Airbnb
Airbnb
1 day ago

Staff Machine Learning Engineer, Traffic Intelligence

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Source: Company careers pageValidated by: Fursa AI
Last checked: 17 hours agoStatus: Live

Job Summary

Architect and maintain end-to-end traffic classification ML systems that distinguish legitimate automation from abusive actors. Build and harden offline-to-online pipelines producing certified source-of-truth datasets, and run rigorous evaluation frameworks (benchmarks and leakage checks). Deploy models with millisecond latency budgets at the internet edge, managing adversarial feedback loops and partnering with security and infrastructure teams to drive down bot-incident MTTM across the fleet.
Location: United States
Workplace: Remote
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Own the end-to-end lifecycle of traffic-scoring models, from problem framing through real-time deployment.
  • •Manage the adversarial feedback loop to improve evasion-resistance and reduce bot-incident MTTM.
  • •Build offline-to-online pipelines that produce certified source-of-truth datasets and defensible evaluation frameworks (e.g., stratified benchmarks and leakage-prevention checks).
  • •Optimize models to meet strict millisecond latency budgets at the internet edge while balancing inference cost and value and maintaining fail-open behavior.
  • •Partner with security analysts, data platform engineers, and infrastructure partners to integrate scoring intelligence into automated mitigation workflows across regions.
Travel: Low travel

Pay and Benefits

Salary: USD 212,000 - 265,000 annually
Equity and Bonus:Equity

Key Requirements

  • •9+ years of applied production ML experience in non-stationary, adversarial domains such as traffic integrity, bot mitigation, or fraud.
  • •Architect scalable offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems.
  • •Strong model evaluation experience using metrics like ROC/AUC, precision/recall, and calibration, with the ability to communicate trade-offs to cross-functional stakeholders.
  • •Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams for reliable production modeling pipelines.
  • •Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior and HTTP/TLS signatures) and how it verifies foundational signals.
Experience:9+ yearsProduction MLAdversarial domainsTraffic integrityBot mitigationFraud
Education:Master's in quantitative field (e.g., Statistics, ML)
Skills:Cross-functional leadershipMentoringTechnical communicationEvaluation rigor
Tech Stack:SQLCDNHTTPTLSLLMBayesian calibrationGraph-based detection

Company Brief

Airbnb
Online marketplace that connects travelers with hosts offering short-term lodging, experiences, and long-term stays. Facilitates booking, payments, and reviews while providing tools for hosts to manage listings globally.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2008
WebsiteLinkedIn