Staff Platform Manager- Payments, Evaluations and Automation

<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><strong>The Community You Will Join:</strong></p> <ul> <li>At Airbnb, if there’s anything related to money, it comes to the Payments team. </li> <li>We are building a world-class payments and commerce organization - one that currently supports 191 countries, 70 currencies, connects dozens of payment providers and banks, processes multiple billions of dollars, and empowers more people to participate in our global marketplace.</li> <li>Our Payments platform organization has 2 domains: Payments and Commerce platform - facilitating money movement and exchange of value while also fueling growth for our business. Payments Compliance and Payments Risk - ensuring safe and efficient payment processing</li> <li>The Staff Product Manager, Evals Automation, reporting to the Product Lead, Payments Risk, will define the strategy and roadmap for how Airbnb evaluates, learns from, and automates risk decisions—ensuring we protect the marketplace while minimizing friction and insult to good users.</li> </ul> <p><strong>The Difference You Will Make:</strong></p> <ul> <li>This role will own the product vision, strategy, and execution for critical evaluation workflows for risk detection mitigation</li> <li>Your mandate is twofold:</li> <ul> <li>Design principled evaluation frameworks that determine the right size and shape of holdouts, control groups, and manual review samples—without degrading model performance or decision quality.</li> <li>Drive automation that meaningfully reduces manual reviews, operational burden, and customer friction, while preserving the labels and signals required to keep models accurate and resilient over time.</li> </ul> <li>Success in this role is measured by sustained reductions in manual review volume, improved approval rates, stable or improving loss performance, and evaluation systems that scale as risk vectors evolve</li> </ul> <p><strong>A Typical Day:</strong></p> <ul> <li>A typical day involves reviewing how models, holdouts, and manual reviews interact across the decisioning funnel—examining false positives, label coverage, approval lift, and downstream loss impact.</li> <li>You will work closely with Data Science, Machine Learning, Risk Engineering, and Operations to understand where current evaluation approaches over-sample good users, introduce bias, or create unnecessary friction.</li> <li>You will define the end-to-end product vision for risk evaluations and learning loops, building a multi-year roadmap that balances statistical rigor, operational efficiency, customer experience, and regulatory expectations.</li> <li>You will own requirements and execution for systems that:</li> <ul> <li>Dynamically size and manage holdouts</li> <li>Optimize when and how manual reviews are invoked</li> <li>Preserve high-quality labels without over-reliance on human review</li> <li>Enable faster, safer iteration on risk models and policies</li> </ul> <li>You will partner deeply with Fraud Safety, Trust, Legal, Policy, Customer Support, and Payments Operations to align on decision principles and ensure evaluation strategies are understood, trusted, and actionable across the company..</li> <li>Collaborate with Payments Operations and Support to reduce manual effort, handle edge cases better, and unlock high-quality decisioning at scale.</li> <li>Key to success for this role is to partner deeply with other Airbnb platform organizations such as Fraud Safety, AirCover, Legal, Customer Support, Policy Enforcement Payments Operations teams to align on th ... (truncated, view full listing at source)