Airbnb Staff Machine Learning Engineer User Listing Marketplace Intelligence 

United States 

975584071

The Difference You Will Make:

There is a huge opportunity to improve the Host and Guest experience by leveraging open source, third party, and home grown ML models. As a staff engineer, you will partner closely with our data science, product partners, and other ML + data engineers on the team to execute on these opportunities in order to improve the Host and Guest product experience on Airbnb.

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.
• 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.

Your Expertise:

• 9+ years of industry experience in applied Machine Learning with a BS/Masters and 6+ years with a PhD.
• Must have experience in both Natural Language Processing and Computer Vision.
• 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), 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.