Tinder Engineering Blog

An AI Day project turned opaque learned representations (embeddings) into a reusable way for the Recommendations and Consumer Research teams to ask better product questions together.

A post about two photo Tinder® features we built on our team (Photo Feedback and a hybrid Smart Photo ordering model), and what it actually took to get them there.

AI coding agents can write code. Merlin turns that capability into a reliable workflow: planning the work, verifying changes, asking for help when needed, and producing results people can review.

How Tinder used AI-powered development workflows to turn one of its most complex iOS upgrades into a faster, safer, and surprisingly scalable migration.

We built an AI agent at a TinderⓇ hackathon that writes automated unit tests, won the engineering track, and deployed it straight into our CI pipeline. Here’s what we learned.

Match Group CEO Spencer Rascoff shares some favorite moments from when workplace language collides with real life, and what it reveals about engineering culture at its best.

How Tinder unified fragmented data models into a single, governed Protobuf system to improve reliability, consistency, and developer velocity at scale.

An inside look at how Tinder’s VLM-based system reframes photo ranking through pairwise comparisons to improve matches, likes, and meaningful connections.

Finding a CTO who balances technical depth with leadership is tough. Read how Tinder’s CEO approached the search, prioritizing hands-on experience, AI readiness, and cultural fit.

Learn about the concrete prompting strategies and techniques that are effective for iOS development at scale.

Monoliths are frustrating for developers, and they can have a profound effect on your organization; they slow down your team and your ability to drive important changes in your codebase.

At Tinder, the quality of your profile photos is paramount. However, asking users to manually sift through their entire camera roll to select images that best represent them can be an overwhelming task.

Tinder ES plugin is a crucial in-house technology that allows us to run complex, observable, efficient and testable scoring algorithms written in Java.

At first glance, Tinder might seem like a simple application. But when you look under the hood, there are a lot of complexities to consider when building the experience that our users know and love today.

Latency in the development loop caused a significant bottleneck in our feature development and reduced overall stability. That's where Ignis came in.

Learn how we streamlined and updated our methodologies to maintain consistency and scalability of the Tinder app.

Through this blog, we will share examples of vulnerable configuration, case-studies with external organizations, mitigation examples, and abuse identification techniques.

This post explores the creation of Obsidian, Tinder's design system.

Learn how we mitigate and prevent accidental indexing of sensitive links.