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

<div class="content-intro"><p><span style="font-family: helvetica, arial, sans-serif; font-size: 12pt;">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.</span></p></div><p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Community You Will Join: </strong></span></p> <p>Listings and Host Tools Data and AI (DnA) team: This team supports host personalization products and provides data driven solutions to achieve superior host experience on Airbnb. These products include but are not limited to managing your space (MYS), host tools etc. We own data pipelines and ML models and will build services for serving that are used in the above areas.</p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Difference You Will Make:</strong></span></p> <p>There is a huge opportunity to improve the Host and Guest experience by leveraging open source, third party, and home grown ML models. As an ML 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.</p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>A Typical Day: </strong></span></p> <ul> <li>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.</li> <li>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.</li> <li>Prototype machine learning use cases for use in the product, and work with stakeholders to iterate on requirements.</li> <li>Develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.</li> <li>Design and build services, API to enable serving ML model driven data to product use cases.</li> </ul> <p><br><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>Your Expertise:</strong></span></p> <ul> <li>8+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.</li> <li>Must have experience in both Natural Language Processing and Computer Vision.</li> <li>Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills</li> <li>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)</li> <li>Experience with 3 or more of these technologies: Tensorflow, PyTorch, Kubernetes, Spark, Airflow (or equivalent), data warehouse (eg. Hive)</li> <li>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.</li> <li>Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)</li> <li>Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.</li> </ul> <p> </p> <p><span style="color: #000000; font ... (truncated, view full listing at source)