# Staff Machine Learning Engineer, Traffic Intelligence

**Company:** [Airbnb](https://hotfix.jobs/companies/airbnb)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $212k – $265k/yr
**Experience:** 9+ years
**Skills:** Machine Learning, Python, SQL, roc/auc, precision/recall, model calibration, feature engineering, Data Pipelines, real-time inference, cdn, load balancers, http/tls, graph detection, bayesian calibration, llm agent tooling
**Posted:** 2026-08-13

> Architects and operates production machine-learning systems that classify web and API traffic, detect bots and scrapers, and support real-time mitigation at internet edge latency. The role requires 9+ years of applied ML experience in adversarial domains and strong expertise in evaluation, data pipelines, and large-scale systems.

## Job Description

## 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.

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