Staff Machine Learning Engineer, Relevance and Personalization

<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><span style="color: #000000; font-family: arial, helvetica, sans-serif;">The Relevance and Personalization team at Airbnb is responsible for search and recommendation across the entire Airbnb digital platform. Be a leader in the team working on critical, impactful projects with focus on developing end-to-end ranking algorithms and ecosystems for optimizing multiple critical business objectives. </span></p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Difference You Will Make:</strong></span></p> <p><span style="color: #000000; font-family: arial, helvetica, sans-serif;">We build cutting-edge AI technologies across the end-to-end search ranking product stack w.r.t. data pipelines, feature and model innovations, serving and experimentation efficiency, leveraging rich signals from various types of data (structured, sequential, image, text, etc) at Airbnb. We collaborate closely with teams across Airbnb to develop the ranking solutions and support a healthy marketplace for hosts and guests to further Airbnb’s mission of creating a world where people can Belong Anywhere. Some past publications from the team can be found here:<a href="https://sites.google.com/view/airbnb-relevance-publications/home"> https://sites.google.com/view/airbnb-relevance-publications/home</a></span></p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>A Typical Day: </strong></span></p> <ul> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">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.</span></li> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">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.</span></li> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.</span></li> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">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.</span></li> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.<br></span></li> </ul> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>Your Expertise:</strong></span></p> <ul> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">9+ years of industry experience in applied Machine Learning, inclusive MS or PhD in relevant fields.</span></li> <li><span style="color: #000000; font-family: arial, helvetica, sans-serif;">Strong programming (Scala / Python / Java / C++ or equivalent) and data engineeri ... (truncated, view full listing at source)