Staff Machine Learning Engineer, Listings and Host Tools Data and AI

Airbnb, founded in 2007, has grown to over 5 million hosts and aims to enhance guest experiences through unique stays. The Staff Machine Learning Engineer will work with the Listings and Host Tools Data and AI team to improve host and guest experiences by developing and productionizing machine learning models and collaborating with cross-functional partners.

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
• 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 machine learning models, drive engineering decisions, and quantify impact
• Prototype machine learning use cases for use in the product, and work with stakeholders to iterate on requirements
• Develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases
• Design and build services, API to enable serving ML model driven data to product use cases

Requirements:

• 8+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields
• 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, state-of-art NLP and CV algorithms) 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), data warehouse (eg. Hive)
• Industry experience building end-to-end Machine Learning infrastructure and/or building and productionizing Machine Learning models, as well as integrating to product use cases
• 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