About the Job
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 conne...
The Growth Platform team’s vision is to drive long term sustainable growth for the Airbnb community. Our mission is to build a best-in-class agentic system, and capabilities to support the growth of all Airbnb products, current and future. We achieve this by delivering highly personalized and relevant content and pr...
The Growth Platform deeply integrates with the Airbnb product, understanding the customer journey and enabling differentiated customer engagement experiences to drive product growth. The platform also powers digital marketing channels including landing pages, email, push, SMS, and digital advertising, as well as the...
Key Responsibilities
Work with large scale structured and unstructured data; explore, experiment, build and continuously improve Machine Learning models and pipelines for Airbnb product, business and operational use cases.
Work collaboratively with cross-functional partners including product managers, operations and data scientists, to identify opportunities for business impact; understand, refine, and prioritize requirements for machine learning, and drive engineering decisions.
Hands-on develop, productionize, and operate ML/AI models and pipelines at scale, including both batch and real-time 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.
Collaborate actively with engineers to apply ML / AI in their solutions to help validate ideas and guide to the right outcomes.
Partner with ML/AI Engineers in foundations engineering to mentor and develop initiatives that make ML/AI applications a core discipline for non-ML/AI engineers.
Required Skills & Abilities
12+ years of industry experience in applied ML/AI, inclusive MS or PhD in relevant fields
Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills
Deep understanding of ML/AI best practices (e.g. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (e.g. neural networks/deep learning, optimization) and domains (e.g. natural language processing, computer vision, personalization, search and recommendation, marketplace optimization, anomaly detection)
Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection) and algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization)
Experience with technologies such as: Tensorflow, PyTorch, Kubernetes, Airflow (or equivalent), Kafka (or equivalent)
Expertise with architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
Agentic and Automation: Experience with AI technologies in automating processes and developing agentic solutions and frameworks.
Agile Practice for AI Production: Experience with the entire AI product development lifecycle from incubation to production at scale, following agile practices in the Applied AI/ML domain.
Infrastructure Acumen: Experience building robust testing frameworks for agent behavior validation and continuous improvement, and driving architectural requirements on ML infrastructures
