招聘
必备技能
Python
Kafka
Spark
Airflow
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:
- The PaymentsAI/ML Foundation team builds the shared platforms, services, and guardrails that enable product and data teams across Airbnb Payments to deliver trustworthy, efficient, and scalable AI capabilities. We provide the infrastructure for training, evaluation, inference, and operations - covering model lifecycle, observability, governance, cost/latency optimization, and developer ergonomics - so teams in Payments can move quickly in AI innovation with confidence.
The Difference You Will Make:
- As a Senior Software Engineer on Payments AI/ML Foundation, you will design and own core platform components that power AI across Airbnb Payments. You will partner with product, infra, data science, and operations to translate ambiguous requirements into robust systems with clear SLAs; build tooling and automation that makes AI development safer and faster; and raise the bar on reliability, performance, and governance for models and agents in production.
A Typical Day:
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Envision, champion, and support the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems.
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Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for AI/ML models, drive engineering decisions, and quantify impact.
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Hands-on productionize and operate AI/ML solution and pipelines at scale, including both batch and real-time use cases.
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Lead, mentor, challenge and grow enthusiastic, collaborative AI/ML culture within the organization
Your Expertise:
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BE/B.tech, preferably in CS, or equivalent qualification.
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7+ years of industry experience in backend/platform engineering (or equivalent), including ownership of production systems.
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Strong programming skills in Python, plus solid data engineering foundations.
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Good to have experience with AI frameworks (Langraph, Strand etc), orchestration (Airflow/Kubeflow), and streaming/processing (Kafka/Spark/Ray).
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Proven track record building observability for AI systems (metrics/logging/traces), with automated alerting, dashboards, and SLO management.
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Experience designing model governance and safety guardrails (prompt/version control, red-teaming, policy checks, approvals).
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Demonstrated ability to drive performance/cost optimizations (profiling, batching, caching, quantization, autoscaling) in high-traffic environments.
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Practical familiarity with vector search/embedding pipelines (indexing strategies, consistency, reindexing, retention).
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.
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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
L2
L3
L4
L5
L6
Mid/L4
Senior/L5
L2 · Financial Analyst L2
0份报告
$211,250
年薪总额
基本工资
$84,500
股票
$105,625
奖金
$21,125
$147,875
$274,625
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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