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:

Trust among the guests and hosts is essential to the success of Airbnb platform. The Trust Screenings team spends a significant amount of time and energy keeping the community safe and thus improving the trust among the parties operating on the Airbnb platform. 

We are responsible for vetting users for safety and brand risk in the physical world (ex: criminal history, redlight workers, human traffickers, etc.,) as opposed to the digital realm (ex: fake inventory, account takeovers, etc.,). We aim to find these users before they complete actions on the platform, such as making a reservation or hosting a listing by running background checks and looking for other physical world signalis that would potentially violate the Airbnb platform policies. 

The Difference You Will Make:

Data available in the physical world to understand the future behavior of guests and hosts of Airbnb platform is very limited. This provides a huge opportunity to mix the data externally and internally to come up with various heuristics and machine learning models to predict the possible future behaviors of the customers. 

As a Staff Machine Learning Engineer on the Trust Screenings team, you will be working with product managers, data scientists, designers, and customer service operations to innovate new ways to predict the physical safety and property damage incidents on the platform.

Problems will be so vague and ambiguous hence you must have the curiosity to dig deep into various patterns/behaviors of the users to understand how and where these life safety incidents may occur. 

Although you will be one of our technical leaders on the team, all individual contributors at Airbnb are Software Engineers which means we expect you to be hands on and contribute code.

A Typical Day: 

• 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.
• Working together with a wide variety of business functions to stop physical safety and property damage incidents in real time.
• Creating new holistic machine learning model detection strategies by collaborating with other trust and safety prevention teams around the Trust Organization.
• 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 fraud detection and mitigation.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.

Your Expertise:

• 8+ years of industry experience in applied Machine Learning, inclusive MS or  PhD in relevant fields
• A Bachelor’s, Master’s or PhD in CS/ML or related field
• Strong programming (Scala / Python / Java/ C++ or equivalent) 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, computer vision, personalization and recommendation, anomaly detection)
• 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.
• Experience with the Trust and Risk domain is a plus.

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.