资深机器学习工程师，流量智能
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Staff Machine Learning Engineer, Traffic IntelligenceAirbnb · 美国
职位信息来自雇主公开的招聘页面。申请前请务必在雇主官网核实详情。
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职位描述
机器翻译Airbnb 诞生于 2007 年，当时两位房东在旧金山的家中接待了三位房客，此后已发展到超过 500 万名房东，在全球几乎每个国家接待了超过 20 亿次房客入住。每天，房东们提供独特的住宿和体验，让房客能够以更真实的方式与社区建立联系。
你将加入的社区：
我们的 Web 和 API 界面处理来自房客和房东的请求，同时还要应对不断增长的自动化代理流量：AI 助手、爬虫和抓取程序。我们构建的系统为这些流量带来清晰度，结合内部 ML 和供应商信号，实时决定如何服务数十亿次日请求。反机器人和反抓取检测是我们最具对抗性的任务，但更广泛的挑战是完整的流量分类：构建评估框架，将合法自动化与滥用行为者区分开来，使高风险决策在整个系统中站得住脚。
你将带来的改变：
你将架构并维护 Airbnb 的端到端流量分类 ML 系统，在高性能模型部署与严格的离线数据管道之间取得平衡。成功与否取决于你强化边缘流量策略的能力——目标是降低机器人事件 MTTM——以及建立严格的评估实践，确保整个系统的基础信号准确性和抗规避能力。
典型的一天：
• 负责流量评分模型的完整生命周期，从问题定义到实时部署，管理对抗性反馈循环以确保高抗规避能力，并直接推动机器人事件 MTTM 的降低。
• 架构稳健的离线到在线管道，生成经过认证的权威数据集，建立严格的评估框架——例如分层基准和防泄漏检查——以确保每一项模型改进都可被实证衡量且经得起检验。
• 在互联网边缘严格的毫秒级延迟预算内执行模型优化，独特地平衡推理成本与增量价值，同时保持全系统范围的故障开放行为。
• 每天与安全分析师、数据平台工程师和国际基础设施合作伙伴协作，将评分智能集成到自动化缓解工作流中，确保尽管存在区域故障转移或 CDN 更新，流量分类仍保持全球一致性。
• 担任团队的机器学习权威，向领导层和跨职能团队传达复杂的模型权衡，将技术研究转化为实用、可扩展的工程指导。
你的专长：
• 9 年以上生产 ML 的应用经验，特别是在非平稳、对抗性领域（例如流量完整性、机器人缓解或欺诈），在其中管理过针对自适应行为者的反馈循环。
• 具有架构可扩展的离线到在线数据管道的实证经验，能够为低延迟推理系统生成经过认证的权威数据集。
• 在严格的模型评估方面有扎实基础，包括 ROC/AUC、精确率/召回率和校准等指标，并能够向跨职能利益相关者传达复杂的权衡。
• 具有大规模数据工程（仓库级 SQL）和高容量事件流特征工程经验，能够构建可靠、生产就绪的建模管道。
• 具备互联网边缘基础设施的实用知识（例如 CDN/负载均衡器行为、HTTP/TLS 签名）及其在验证基础信号中的作用。
• 具有跨职能领导力的可靠业绩记录，通过共享数据集和消费方契约落地项目，同时在技术质量和设计实践方面指导初级工程师。
• 定量领域（例如统计学、ML）的硕士/博士学位或同等深厚的工程经验，并对衡量抗规避能力的大规模系统有重要主导经验。 优先条件：
• 统计学、数学、机器学习或相关定量学科的博士学位。
• 在基于图的协同或 Sybil 网络检测方法方面具有高级专业知识，用于复杂分布式系统分析。
• 在因果或计量经济学方法方面有深厚经验，用于建模误报对合法用户流量的业务影响。
• 具有实施贝叶斯校准技术以处理对抗性偏差、稀疏或不平衡数据集的经验。
• 熟悉数据治理实践和平台工程，特别是管理经过认证数据集的生命周期和下游消费方契约。
• 接触过 LLM 代理工具和基准测试，重点关注在延迟和价值权衡下优化推理成本。
你的工作地点：
该职位为美国 - 可远程。该职位可能包括偶尔在 Airbnb 办公室工作或参加异地活动，具体与你的经理商定。虽然该职位可远程，但你必须居住在 Airbnb, Inc. 拥有注册实体的州。点击此处查看最新的排除州列表。该列表持续变化，因此如果你居住的州在排除列表中，请再次与我们确认。如果你的职位受雇于另一家 Airbnb 实体，你的招聘人员将告知你可以从哪些州工作。
我们对包容与归属的承诺：
Airbnb 致力于与尽可能广泛的人才库合作。我们相信多元化的想法能够促进创新和参与，并使我们能够吸引具有创造力的领导者，并开发最好的产品、服务和解决方案。鼓励所有符合条件的个人申请。
我们也努力提供对残障人士包容的申请和面试流程。如果你是残障候选人，并且为了提交申请需要合理便利，请通过以下方式联系我们：reasonableaccommodations@airbnb.com 。请包括你的全名、你申请的职位以及协助你完成招聘流程所需的便利。
我们请求你仅在你因残障而无法完成我们的在线申请时才联系我们。 我们将如何照顾你：
我们的职位名称可能跨越多个职业级别。实际基本工资取决于许多因素，例如：培训、可迁移技能、工作经验、业务需求和市场需求。基本工资范围可能会发生变化，并可能在未来修改。该职位也可能有资格获得奖金、股权、福利和员工旅行积分。
薪资
$212,000 — $265,000 USD
合理便利：我们致力于在整个招聘过程中为残障申请人提供合理便利。如果你需要帮助或便利，请在有人就职位联系你时告知你的招聘人员。
关于招聘诈骗的说明：诈骗者有时会冒充 Airbnb 招聘人员，从候选人那里获取金钱或个人信息。对于 Airbnb 的招聘流程，有几件事始终成立：我们的空缺职位发布在 Airbnb 的职业页面上，网址为 careers.airbnb.com，我们的招聘人员仅通过 @ airbnb.com 或 @ ext.airbnb.com 电子邮件地址联系。我们的招聘人员在你面试期间绝不会询问你的社会安全号码、银行账户详细信息、护照或支付应用信息。我们也绝不会在面试过程中要求你支付费用、汇款、存入或兑现支票，或购买工作相关设备（例如公司笔记本电脑）。我们鼓励候选人对此类招聘诈骗保持警惕，如果你不相信某个人确实隶属于 Airbnb，请不要分享敏感信息。
以上内容由机器翻译自动生成，可能存在错误；投递前请以雇主原文为准。
查看雇主原文
职位描述
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. 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.
薪资
$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.