招聘
必备技能
Machine Learning
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:
We connect Airbnb’s community with the right information, in the right place, at the right time. We tailor Messaging & Notifications so hosts on Airbnb can streamline their operations, and travelers get just the information they need to enjoy their stay worry-free. Additionally, we are building new connections within our community to help enrich the experience of hosting & traveling on Airbnb: easing the process of hosting, and adding meaning to our guest’s trips.
The data team utilizes industry-leading tools, builds scalable data systems and applies cutting-edge ML models to provide insights and empower all products in the Communication and Connectivity (CnC) organization.
The Difference You Will Make:
At CnC, data is foundational to our organization’s success.
This role will lead key initiatives to design and build large-scale, distributed data systems - both batch and real-time processing. The data will power machine learning models and unlock new product features. You’ll be at the center of cross-functional collaboration, bridging backend, frontend/client, and machine learning engineering teams.
CnC is applying GenAI and large language models (LLMs) to power products that enhance the Airbnb experience in various surfaces including highly used ones like Messaging. We're building a robust ML platform to power our product ambitions.
A Typical Day:
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Shape the team’s long-term vision and roadmap in close collaboration with cross-functional partners across Airbnb
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Build strong relationships with partner engineering teams, including backend, client, data science, analytics, and ML, to drive aligned and impactful outcomes
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Design, develop, and maintain reliable, scalable data pipelines - both batch and real-time - that collect, process, and serve data from diverse sources across Airbnb
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Implement robust offline and online feature building processes to enable faster production of ML products
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Architect and build ML infra and optimize for performance, scalability, and cost-effectiveness
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Mentor and develop engineers on the team, while also contributing to and influencing the broader data engineering community at Airbnb
Your Expertise:
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9+ years of relevant industry experience with a Bachelor’s and/or Master’s degree in CS/EE, or equivalent experience, or 6+ years of experience with a PhD
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Strong CS fundamentals, and knowledge of architecture and common design patterns
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You’re passionate about being in a product-focused environment where everyone cares deeply about customer impact
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You have experience of running data processing pipelines in production using distributed data processing frameworks like Apache Spark or Flink
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Experience collaborating with client, backend, ml, analytics teams, product and business partners
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Effectively work across team boundaries to establish overarching data architecture, data flow, and provide guidance to individual teams
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Experience working on/with end-to-end Machine Learning products
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Excellent communication skills, both written and verbal
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.
Pay Range**$204,000—$255,000 USD**
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关于Airbnb

Airbnb
PublicAn online community marketplace for people to list, discover, and book accommodations through mobile phones or the Internet.
5,001-10,000
员工数
San Francisco
总部位置
$75B
企业估值
评价
3.7
10条评价
工作生活平衡
3.2
薪酬
3.5
企业文化
4.1
职业发展
3.0
管理层
2.8
68%
推荐给朋友
优点
Great team culture and colleagues
Flexible work arrangements and remote options
Good benefits and perks
缺点
Work-life balance challenges
Communication issues and lack of direction
Fast-paced and stressful environment
薪资范围
37个数据点
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Junior/L3 · Data Scientist L3
0份报告
$240,579
年薪总额
基本工资
-
股票
-
奖金
-
$204,492
$276,666
面试经验
2次面试
难度
3.5
/ 5
时长
14-28周
录用率
50%
体验
正面 50%
中性 50%
负面 0%
面试流程
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Interviews
6
Behavioral Interview
7
Offer
常见问题
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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