Manager, Machine Learning Engineering - 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! As a Machine Learning Engineering Manager on the team, you’ll lead a group of ML engineers and set direction for projects that power search and recommendations across the entire Airbnb platform—directly influencing how guests and hosts connect in meaningful ways. Come lead the design and deployment of state-of-the-art ranking algorithms and build resilient, high-performing teams and 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. In this role, you’ll amplify impact through technical leadership—setting strategy, raising engineering quality, and ensuring the team delivers reliable results at scale. You’ll still stay close to the work—guiding architecture and critical technical decisions—while enabling engineers to do their best work.</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. You’ll also be accountable for team health: hiring, coaching, performance management, and building an inclusive culture. 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>Work with large scale structured and unstructured data, provide technical leadership to build and continuously improve cutting edge Machine Learning (ML) 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, set team priorities and roadmap, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.</li> <li>Lead the delivery of ML/AI models and pipelines at scale, including both batch and real-time use cases, ensuring operational excellence (on-call health, reliability, cost, latency, and model quality).</li> <li>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, and drive adoption of best practices across the team.</li> <li>Example projects include: feature platform, model interpretability, hyperparameter optimization, concept drift detection.</li> <li>Hire, coach, and develop engineers; set clear expectations; conduct performance management; and build ... (truncated, view full listing at source)