Meta Engineering
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Meta EngineeringLabsF3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale Facebook Feature Framework (F3) is Meta’s framework for authoring, compiling, and serving the machine learning features underlying ads ranking models, where a single request can require more than…
Meta EngineeringLabsCatching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale Over the last year we’ve been sharing our work leveraging large language models to catch bugs on the fly — a novel approach we call Catching Just-in-Time Tests (JiTTests).
Meta EngineeringLabsMeta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design This post describes the next generation of Meta Andromeda, Meta’s personalized ads retrieval engine, and how co-designing the model, system, and GPU hardware unlocks a new efficiency frontier for ads…
Meta EngineeringLabsPrivacyLib: Privacy Controls at the Data Access Boundary This latest deep dive into Meta’s Privacy Aware Infrastructure (PAI) takes a look at PrivacyLib. A privacy requirement must hold as data moves through Meta’s diverse data and processing systems.
Meta EngineeringLabsPython Typing Survey 2026: AI Tools and the Shifting Checker Landscape The 2026 Python Typing Survey received 1,794 responses. Type hint use changed little across three years, but checker selection changed sharply.
Meta EngineeringLabsMeta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks At the 2026 Open Compute Project (OCP) Global Summit, Meta shared the networking innovations that are directly supporting our latest AI models, including Muse Wonder and Muse Spark , and our…
Meta EngineeringLabsGenRecs Tokenization: Transforming Ads Recommendation at Meta with Stable Model Vocabulary Generative Recommenders (GenRecs) represent a paradigm shift from scoring every ad to reasoning about ad content and user preferences together in order to predict the best outcome for advertisers.
Meta EngineeringLabsThe Thin Orchestrator: How We Made Our AI Agents More Reliable by Giving the Router Less to Do Sometimes, giving an AI agent all of the knowledge it needs is the wrong approach. At Meta, we’ve seen notable improvements in efficiency and accuracy by keeping the orchestration layer of our data…
Meta EngineeringLabsInside Prometheus: Meta’s Journey to a Gigawatt-Scale Supercluster With the recent release of the Muse family of foundational models, we want to take you inside the infrastructure that has made it all possible.
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