Airbnb was born in 2007 when two Hosts welcomed three guests to their San Francisco home, and has since grown to over 4 million Hosts who have welcomed more than 1 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 Airbnb Community Support (CS) team is a global business unit that provides support to our community of millions of guests and hosts who embark on trips and participate in experiences around the world every year. Within CS, the Core Machine Learning team is the core team responsible for driving CSxAI (Customer Support x Artificial Intelligence) initiatives by adopting the Generative AI to enable an intelligent, scalable and exceptional service experience. The team develops and enhances task-specific Large-Language-Models (e.g. Text Summarization models, Multi-turn Dialogue Language models) and foundational machine learning models (e.g. Named-Entity Recognition, Sentiment modeling) for a wide range of applications in Airbnb. Here are some Medium posts from the team: How AI Text Generation Models Are Reshaping Customer Support at Airbnb, Task-Oriented Conversational AI in Airbnb Customer Support.

The Difference You Will Make:

As a staff machine learning engineer, your expertise will be pivotal in developing Conversational AI solutions and other cutting-edge Machine learning techniques to define and shape the future of the Airbnb Community Support experience. You will also partner with product managers, software engineers, and operation teams to leverage engineering innovations to simplify the business requirements into scalable solutions. 

A Typical Day: 

• Design, develop, productionize and operate Machine learning models, including Large-Language-Models, and pipelines at scale, for both batch and real-time use cases.
• Collaborate with machine learning infrastructure engineering teams to evolve how we build reusable and scalable AI solutions for Airbnb products. 
• Work with large scale structured and unstructured data, build and continuously improve cutting edge machine learning models for Airbnb product, business and operational use cases.
• Leverage third-party and in-house Machine Learning tools & infrastructure to develop reusable, highly differentiating and high-performing machine learning systems, enable fast model development, low-latency serving and ease of model quality upkeep.
• Work collaboratively with cross-functional partners including product managers, operations and data scientists, identify opportunities for business impact, understand and prioritize requirements for machine learning systems and data pipelines, drive engineering decisions and quantify impact.

Your Expertise:

• 9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• Strong programming (Python/Java) and data engineering skills
• Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. natural language processing, personalization and recommendation)
• Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), Kafka (or equivalent), data warehouse (eg. Hive).
• Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models.
• Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
• Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.

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.  

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—$259,000 USD