Job description

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Our web and API surfaces handle requests from guests and hosts alongside a growing volume of automated agents: AI assistants, crawlers, and scrapers. We build the systems that bring clarity to this traffic, combining in-house ML and vendor signals to decide in real time how to serve billions of daily requests. Anti-bot and anti-scraping detection is our most adversarial mandate, but the wider challenge is full traffic classification: building evaluation frameworks that tell legitimate automation apart from abusive actors, so high-stakes decisions hold up across the fleet. The Difference You Will Make: 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. A Typical Day: Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM. Architect robust offline-to-online pipelines that produce certified source-of-truth datasets, establishing rigorous evaluation frameworks—such as stratified benchmarks and leakage-prevention checks—to ensure every model improvement is empirically measurable and defensible. 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. Partner daily with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows, ensuring global consistency in traffic classification despite regional failovers or CDN updates. 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. Your Expertise: 9+ years of applied experience in production ML, specifically within non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud) where you have managed the feedback loop against adaptive actors. 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. Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers on technical quality and design practices. MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience, with significant ownership of large-scale systems measuring evasion-resistance. Preferred: PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline. Advanced expertise in graph-based coordination or Sybil network detection methods for complex, distributed system analysis. Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic. Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets. Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts. Exposure to LLM agent tooling and benchmarking, with a focus on optimizing inference costs against latency and value trade-offs. Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool possible. We believe diverse ideas foster innovation and engagement, and allow us to attract creatively-led people, and to develop the best products, services and solutions. All qualified individuals are encouraged to apply. We strive to also provide a disability inclusive application and interview process. If you are a candidate with a disability and require reasonable accommodation in order to submit an application, please contact us at: reasonableaccommodations@airbnb.com . Please include your full name, the role you’re applying for and the accommodation necessary to assist you with the recruiting process. We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application. Equal Employment Opportunity : Airbnb values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Airbnb are considered without regard to race, color, religion, national origin, age, gender, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, gender expression, sexual orientation, or any other legally protected characteristic. How We'll Take Care of You: Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits. Pay Range $212,000 — $265,000 USD Reasonable Accommodations: We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role. A Note on Recruiting Scams: Scammers sometimes pose as Airbnb recruiters to get money or personal information from candidates. A few things that will always be true for Airbnb’s hiring process: our open roles are posted on Airbnb’s Career’s Page at careers.airbnb.com, and our recruiters correspond only from @ airbnb.com or @ ext.airbnb.com email addresses. Our recruiters will never ask for your Social Security number, bank account details, passport, or payment app information while you are interviewing. We’ll also never ask you to pay a fee, send money, deposit or cash a check, or purchase work-related equipment (such as a company laptop) during the interview process. We encourage candidates to remain vigilant of these recruiting scams and not share sensitive information if you do not believe an individual is actually affiliated with Airbnb.

Requirements

Must-haves

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.
Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers on technical quality and design practices.
MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience, with significant ownership of large-scale systems measuring evasion-resistance.
We ask that you only reach out to us if you are a candidate whose disability prevents you from being able to complete our online application.

Nice-to-haves

PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.
Advanced expertise in graph-based coordination or Sybil network detection methods for complex, distributed system analysis.
Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic.
Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets.
Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts.
Exposure to LLM agent tooling and benchmarking, with a focus on optimizing inference costs against latency and value trade-offs.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

apiaimlmachine learningsqlllm

Platform: greenhouse
Effort: quick

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