トレンド企業

Airbnb
Airbnb

Belong anywhere.

Machine Learning Engineer, Customer Support Engineering

職種機械学習
経験ミドル級
勤務地Remote-USA
勤務リモート
雇用正社員
掲載1週間前
応募する

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:

Machine Learning and Artificial Intelligence are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb.
The Core ML team in Community Support is the team responsible for adopting the Agentic AI technologies to enable an intelligent, scalable and exceptional customer service experience. We are responsible for developing the Chat AI assistant, Voice AI Assistant and more! The team is constantly exploring the SOTA Agentic architecture, develops and enhances various AI models, ML services and leverages tools including SFT, Reinforcement learning, Distillation, RAG/Search, LLM evaluation and testing automation, feedback-based learning and guardrail for a wide range of applications in Airbnb.

The Difference You Will Make:

We believe our current customer experiences in these domains are only scratching the surface of the innovations that are possible, and that science is at the heart of delivering a step-function change for our Guest and and Host on Airbnb.
You will build and leverage cutting edge AI technologies to transform Airbnb’s customer service by delivering personalized, easy-to-use and proactive customer service experience.
Many of the initiatives you’ll tackle are in their early conceptual stages. You will have the opportunity to shape these ideas from inception to production, turning visionary concepts into impactful realities.

A Typical Day:

  • Champion the development of novel ML systems, product integrations, and performance optimizations to solve real-world problems
  • Work cross-functionally with product, design, and other engineering counterparts to design and build efficient AI solutions for Airbnb CS products
  • Learn and share the latest AI/ML technologies with the team.

Your Expertise:

  • PhD or Master's degree w/ 3+ YOE in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field — or equivalent industry experience
  • Hands-on expertise in LLM, including pretraining, fine-tuning (SFT, RLHF, GRPO), prompt engineering, RAG architectures, and LLM evaluation frameworks
  • Experience building Agentic AI systems — including multi-agent orchestration, tool-use, planning, memory, and autonomous reasoning pipelines (e.g., Re Act, Lang Graph, Auto Gen, or similar)
  • Experience of shipping production-grade ML/AI systems at scale, with deep understanding of ML infrastructure, model serving, and MLOps best practices
  • Excellent communication skills with the ability to collaborate effectively across Engineering, Product, and Design organizations

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

$162,000—$186,000 USD

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Airbnbについて

Airbnb

Airbnb

Public

An online community marketplace for people to list, discover, and book accommodations through mobile phones or the Internet.

5,001-10,000

従業員数

San Francisco

本社所在地

$75B

企業価値

レビュー

10件のレビュー

3.7

10件のレビュー

ワークライフバランス

3.2

報酬

3.5

企業文化

4.1

キャリア

3.0

経営陣

2.8

68%

知人への推奨率

良い点

Great team culture and colleagues

Flexible work arrangements and remote options

Good benefits and perks

改善点

Work-life balance challenges

Communication issues and lack of direction

Fast-paced and stressful environment

給与レンジ

37件のデータ

Junior/L3

L2

L6

M3

M4

M5

M6

Mid/L4

Principal/L7

Senior/L5

L3

L4

L5

Junior/L3 · Data Scientist L3

0件のレポート

$240,579

年収総額

基本給

-

ストック

-

ボーナス

-

$204,492

$276,666

面接レビュー

レビュー2件

難易度

3.5

/ 5

期間

14-28週間

内定率

50%

体験

ポジティブ 50%

普通 50%

ネガティブ 0%

面接プロセス

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Onsite/Virtual Interviews

6

Behavioral Interview

7

Offer

よくある質問

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Culture Fit