Senior Machine Learning Engineer, Listing Integrity

Airbnb

1d•Remote

About The Position

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: Imagine getting off a long international flight, standing in the freezing rain, attempting to contact your Airbnb host, only to realize that they are not responding because the Airbnb that you booked was not real. The Listing Integrity team’s ultimate goal is to prevent experiences like this by proactively detecting and removing fraudulent listings (Homes, Experiences, and Services) so that guests can search and book on Airbnb with confidence. The team uses data drive heuristics, machine learning models, and customer service operations in order to accomplish this goal. The Difference You Will Make: As a Senior Machine Learning Engineer on the Listing Integrity team, you will be working with data scientists, designers, product managers, and customer service operations to innovate new ways we can stop bad actors in the ever evolving fraudulent listing creation and financial losses associated with it by collaborating across team boundaries. On this team, you must have the curiosity to dig deep into various end to end systems in order to understand how and where the fraud occurs. Your curiosity will be rewarded with finding projects that have outsized impacts on decreasing fraud losses and protecting Airbnb users from bad experiences while on vacation. 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 fraud attacks in real time. Collaborate with Data Scientists and other Machine Learning Engineers across trust to come up with the best modeling strategy and approach to defending against fake listings. 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. Examples include: ML models to detect Fake Listing creation attempts.

Requirements

5+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
Passion for building user-facing products or large backend systems.
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)

Nice To Haves

Experience with the Trust and Risk domain is a plus.

Responsibilities

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 fraud attacks in real time.
Collaborate with Data Scientists and other Machine Learning Engineers across trust to come up with the best modeling strategy and approach to defending against fake listings.
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.