Engineering at Meta Blog
WhatsApp is committed to helping people stay safe while protecting the privacy of their messages. As scam tactics evolve — from impersonation to social engineering to AI-generated lures — we re always evolving as well, so that our protections stay ahead of scammers while protecting people’s personal messages with end-to-end encryption. Today, we re sharing an early [...] Read More... The post How…
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read…
Meta s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The…
Hierarchical Interest Representation is a research area for Meta Ads. We’re exploring an upstream representation layer over the universe of Ads entities – users, advertisers, products, services – learning unified embeddings that connect users inferred interests with the breadth of what advertisers offer in their deep funnel ads. The innovations in Hierarchical Interest Representation are [...]…
TL; DR At Meta s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance. When a Linux kernel upgrade risked regressing latency across Meta s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to build a scheduling policy customized to the Ads delivery [...] Read More... The post Modernizing the…
Over the past several years, model capabilities and training dataset sizes have experienced exponential growth. During the past year or so, the time between new-frontier-model releases has gone down from months to weeks. Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation. If AI is [...] Read More... The post Meta s AI Storage Blueprint…
This year marks Meta s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world s most influential programming languages, and we use it across our engineering stack, from [...] Read More... The…
Privacy controls — systems that enforce retention, access, allowed-purpose, downstream-sharing, or anonymization policies — require a reliable understanding of data to function. Before such a control can operate effectively, it must know exactly what it is looking at. This can be complex, as demonstrated by a field simply named age : In one context, it [...] Read More... The post Privacy-Aware…
Smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards need to pack enough energy to power features like cameras, speakers, AI workloads, and even a display. But it all has to fit into the glasses’ temple arms. So how do you place a battery with enough power to run a pair of smart glasses [...] Read More... The post How Meta Engineered Ultra-Narrow Batteries for AI Glasses appeared first on…