Senior Software Engineer, Experimentation Platform

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. The Community You Will Join The Experimentation team's mission is to provide an insightful, reliable and easy to use experimentation platform (A/B testing and feature rollout) for customers across the company. Thousands of experiments run on our platform each year. Our team is multidisciplinary, owning a vertical stack that includes UIs for setting up and analyzing experiments, data pipelines and libraries for calculating results, and the clients and services that deliver feature flags, product configuration and experiment assignment to applications. You will work closely with data science partners, engineers across infrastructure teams, and the product engineers who use our tools every day. The Difference You Will Make You will co-own the client libraries and services that thousands of Airbnb engineers use to roll out features and run experiments, primarily on the JVM, alongside the engineer who owns them today. You will help consolidate our feature flag and product configuration systems onto a single platform and make it easy and safe to set up flags, configuration and experiments in code. Success looks like clients that are fast, reliable and easy to adopt, large cross-team migrations that finish on schedule without breaking consumers, and new assignment capabilities that meet Airbnb's needs. A Typical Day Build and extend the capabilities of our experimentation, feature flagging and product configuration clients and the services behind them. Design and lead cross-team migrations: evolving client APIs, deprecating old ones, and moving consuming services over safely. Improve the testability, performance and reliability of client libraries that are widely deployed and tightly interconnected. Build new allocation and targeting capabilities in partnership with data science, such as holdouts and mutually exclusive experiments, with attention to statistical pitfalls. Use AI-assisted development to move faster on migrations, tests and refactors, and help the team set good practice for it. Participate in all phases of software development from design through implementation, testing and on-call, and work...

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