Snap Engineering Blog

Shared Augmented Reality experiences are part of Spectacles’ mission to connect humans in the real world.

Our advertising platform at Snap receives a large volume of new ads every day. To maintain a high-quality and relevant ad ecosystem, we need to review these creatives at scale, to ensure they align with our policies, are safe for our community, and are shown to the right Snapchatters.

Building ML at scale: Discover how Snap's Bento ML Platform powers personalized user experiences through cutting-edge ML Ops

At Snap, we want to empower you to learn about the world and have fun with other Snapchatters. On Spotlight, Snap’s short-video platform, we personalize what you see based on your interests and previous watch history. This blog discusses the challenges we faced and techniques we applied for large-scale embedding-based retrieval, which selects the stories that users may like from a large candidate pool in real time.

At Snap, privacy is a central part of our product philosophy. We want Snapchatters to feel safe and secure interacting with their friends and exploring the world around them without worrying about the privacy of their data. To achieve this goal one of the tools we use is Differential Privacy (DP), the de facto privacy standard in both industry and academia. In this article, first we summarize some of the privacy concerns that Snap and the industry as a whole face. Then we examine how Snap uses DP to address those concerns regarding friend recommendations and Maps features.

Behind the lens: How supplemental feeds help advertisers bring the AR experience to Snap ads and reach millions of Snapchatters.

At Snap, we have a strong focus on user experience and have built many ML applications to surface the right content to our users. Historically, our engineers have spent a significant amount of time working with features. We’ve found that by automating the creation and consumption of a few common features types, we can simplify the majority of feature engineering work. This provides an opportunity for building a general-purpose feature platform that benefits many business critical ML use cases.

At Snap we develop a large number of products and solutions powered by ML. Modern ML models require tons of high-quality data to be collected, labeled and processed. All these processes may frequently take a  long time and lots of resources. In this post we talk about how we leverage synthetic data to boost the development of our ML models.

Compression is broadly used across web and mobile applications, and Snap is no exception. Video compression is one of the core technical considerations at Snap. It enables our users to share and enjoy the moments in daily lives. In this post we’ll talk a bit about the fundamentals of compression, and then describe how we’ve recently invested in improving compression for video to achieve performant, high quality results.

How do we ensure consistency, accuracy, and relevancy? And more importantly, how do we ensure operating measures - metrics - already in use are trustworthy and measuring as intended? Social Technology Operating Measures Principles - or STOMP - is a set of principles to help standardize the measurement and qualification of metrics.