# Staff Machine Learning Engineer, Traffic Intelligence at Airbnb

- Company: Airbnb
- What the company does: Get an Airbnb for every kind of trip → 8 million vacation rentals → 2 million Guest Favorites → 220+ countries and regions worldwide. Backed by General Catalyst, Greylock and Kleiner Perkins.
- Company website: http://www.airbnb.com/
- Type: Startups (AI role)
- Level: Senior
- Location: United States
- Work setup: On-site
- Posted: 2026-08-13
- Apply by: 2026-10-12
- Apply: https://careers.airbnb.com/positions/8129371?gh_jid=8129371
- Page: https://www.1752.vc/careers/jobs/airbnb-staff-machine-learning-engineer-traffic-intelligence/

## About the role

You will architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines. Success is measured by your ability to harden edge-traffic policies—targeting reduced bot-incident MTTM—and by establishing rigorous evaluation practices that ensure foundational signal accuracy and evasion-resistance across the fleet.

## What they're looking for

- Execute model optimization within strict millisecond latency budgets at the internet edge, uniquely balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors
- Serve as the team’s machine learning authority, communicating complex model trade-offs to leadership and cross-functional teams to translate technical research into practical, scalable engineering guidance
- Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems
- Strong foundation in rigorous model evaluation, including metrics like ROC/AUC, precision/recall, and calibration, with an ability to communicate complex trade-offs to cross-functional stakeholders
- Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams to build reliable, production-ready modeling pipelines
- Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures) and their role in verifying foundational signals

Tags: Software Engineering