Senior 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>Join Airbnb’s Relevance and Personalization team, where you’ll have a unique opportunity to shape the discovery experience for over 150M global users! You’ll take the lead on projects that power search and recommendations across the entire Airbnb platform—directly influencing how guests and hosts connect in meaningful ways. Come design and deploy state-of-the-art ranking algorithms, deploying robust systems that optimize Airbnb’s most important business goals. </p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>The Difference You Will Make:</strong></span></p> <p>Our team pushes the boundaries of AI and machine learning throughout the search ranking stack, from data pipelines to feature engineering, model innovation, real-time serving, and large-scale experimentation. You’ll work hands-on with diverse data sources—structured, behavioral, image, and text—transforming raw information into intelligent signals that drive relevance and personalized recommendations.</p> <p>Collaboration lies at the heart of our culture. You’ll partner with talented engineers, data scientists, product managers, and designers from across Airbnb to develop holistic solutions that ensure a vibrant and equitable marketplace. Together, we’re dedicated to advancing Airbnb’s mission to create a world where anyone can belong anywhere.</p> <p>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></p> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>A Typical Day: </strong></span></p> <ul> <li style="font-weight: 400;">Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning (ML) models for Airbnb product, business and operational use cases.</li> <li style="font-weight: 400;">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 style="font-weight: 400;">Hands-on develop, productionize, and operate ML/AI models and pipelines at scale, including both batch and real-time use cases.</li> <li style="font-weight: 400;">Leverage third-party and in-house ML/AI 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.</li> <li style="font-weight: 400;">Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.</li> </ul> <p><span style="font-family: arial, helvetica, sans-serif; color: #000000;"><strong>Your Expertise:</strong></span></p> <ul> <li>12+ years of industry experience in applied ML/AI, inclusive MS or PhD in relevant fields.</li> <li>Strong programming (Scala / Python / Java / C++ or equivalent) and data engineering skills.</li> <li>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, optimiza ... (truncated, view full listing at source)