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Engineering at Meta

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  • Engineering at MetaCompany/Startup Blogs9 min read

    Intelligent Delivery: Making Every Unit of Compute Serve Better Ads

    Ads ranking models are growing toward LLM scale faster than the hardware to run them can keep up. An extra unit of compute has more impact on some ad requests, models, and chips than on others. The challenge is deciding where to allocate it to get the best outcome for the entire system. We built [...] Read More... The post Intelligent Delivery: Making Every Unit of Compute Serve Better Ads appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs8 min read

    Your Algorithm: Giving People More Ways to Personalize Facebook Recommendations

    Your algorithm is a suite of AI-powered tools that makes Facebook recommendations more transparent and steerable, with a single editable view of the interests that shape Feed and Reels. We’re sharing how Your algorithm turns people’s natural language preferences into responsive recommendations, and how LLMs are tailored to specific tasks within the system. Traditionally, the [...] Read More... The post Your Algorithm: Giving People More Ways to Personalize Facebook Recommendations appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs17 min read

    How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale

    Triton Low-Level Extensions (TLX), is an extension to Triton that helps engineers optimize GPU performance while preserving Triton’s productive programming model. TLX introduces explicit warp-group and cluster-level orchestration, letting developers adopt new hardware capabilities without sacrificing velocity. TLX has demonstrated performance improvements in RecSys training, RecSys inference, and large-scale LLM pretraining. Modern GPU architectures increasingly [...] Read More... The post How We Built TLX: A Hardware-Native GPU Compiler for Production ML at Scale appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs6 min read

    Extending LLDB to Debug Applications That Span CPUs and GPUs

    We built a vendor-neutral accelerator plugin architecture in lldb-server for debugging applications that span CPUs and GPUs. It separates shared debugger infrastructure from hardware-specific device control. The design represents the CPU and GPU as separate debugger processes while presenting them in one debugging session. Modern AI applications rarely execute in one place. A workload can [...] Read More... The post Extending LLDB to Debug Applications That Span CPUs and GPUs appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs10 min read

    F3 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 20,000 features. We first presented F3 at AI @ Scale 2020. F3 reduced execution computational costs by compiling all features into a single merged graph, but that [...] Read More... The post F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs6 min read

    Catching 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). Now, we’re sharing the results and positive impact we’ve seen with Catching JiTTests deployed into production. Our paper, “Just-in-Time Catching Test Generation at Meta,” fully details [...] Read More... The post Catching Bugs Before They Land: JiTTesting’s Human-on-the-Loop Approach at Full Scale appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs12 min read

    Meta 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 retrieval. In production, the system scores every eligible ad with near ranking-level model performance at retrieval scale and surfaces the top candidates at over one [...] Read More... The post Meta Andromeda: Powering the Next Generation of Ads Retrieval Through Model-System Co-Design appeared first on Engineering at Meta .

  • Engineering at MetaCompany/Startup Blogs18 min read

    PrivacyLib: 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. Historically, supporting a new requirement meant implementing the same enforcement logic separately in each system. PrivacyLib replaces this with a common library that runs inside [...] Read More... The post PrivacyLib: Privacy Controls at the Data Access Boundary appeared first on Engineering at Meta .