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Distributed Systems

Meta Engineering Blog

26 articles on EngBrief

Engineering at Meta (formerly Facebook Engineering) covers how Meta builds products used by billions of people. Posts explore large-scale distributed systems, AI/ML research and production, mobile performance, data infrastructure, networking, and open source projects like React, PyTorch, and Llama.

Distributed SystemsAI/MLOpen SourceMobile PerformanceData Infrastructure
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Latest Articles

Engineering6d ago

Lights Out, Systems On: Validating Instant Power Loss Readiness

Meta's engineering team developed Instantaneous PowerLoss Storm, a testing paradigm to validate readiness for instant power loss in data centers. To build readiness, they integrated power loss tolerance into their DC stack using defense-in-depth strategies, such as in-memory data persistence and asynchronous signaling mechanisms. Validation involved a controlled power supply fault injection and remedial actions to ensure seamless region de-energization. Key tradeoffs were made to balance reliability and velocity of growth, including prioritizing critical infrastructure impacts and tolerating transient service errors. Validation exercises, including de-energizing production regions, successfully trained infrastructure and engineers to handle region-level failures.

SocialScale
1 min
Engineering14d ago

SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems

Here's a 3-sentence summary of the SilverTorch engineering blog post: SilverTorch, a unified model-based system, improves recommendation quality and efficiency by integrating all retrieval components into a single neural network architecture called Index as Model. This design boosts throughput up to 23.7x and compute cost efficiency up to 20.9x compared to traditional multi-service approaches, while maintaining sub-100 millisecond latency. By expressing different microservices as model modules within a single neural network, SilverTorch enables joint optimization of filtering, search, and scoring operations, improving the quality of recommendations for platforms serving millions of users.

SocialScale
1 min
Engineering27d ago

Reel Friends: Building Social Discovery that Scales to Billions

Engineers on the Facebook Reels team developed the Friend Bubbles feature, which highlights friends' Reels interactions, using a machine learning model that evolved over time. The feature requires complex engineering work to balance behaviors between iOS and Android users, who have distinct interaction patterns. Ultimately, the team discovered a key factor that enabled the feature to function seamlessly, underscoring the importance of nuanced understanding in engineering seemingly simple features.

SocialScale
1 min
Engineering28d ago

Migrating Data Ingestion Systems at Meta Scale

Meta revamped its data ingestion system to enhance reliability at scale. The new architecture shifted from customer-owned pipelines to a self-managed data warehouse service, improving efficiency and scalability. To achieve a successful migration, Meta established a clear migration job lifecycle, verified jobs for correctness, and implemented robust rollout and rollback controls to handle issues during the transition.

SocialScale
1 min
Engineering29d ago

Labyrinth 1.1: Making End-to-End Encrypted Backups Even More Reliable

Labyrinth 1.1 improves the reliability of end-to-end encrypted backups in Messenger by allowing messages to reach the encrypted backup in real-time, rather than waiting for the device to come back online. This is achieved through a new sub-protocol that ensures messages survive device loss, changes, and extended sign-in gaps. The update enhances the security and integrity of encrypted message history, making it more accessible to users across devices.

SocialScale
1 min
EngineeringMay 1, 2026

How Meta Is Strengthening End-to-End Encrypted Backups

Meta has strengthened its end-to-end encrypted backup system by implementing over-the-air fleet key distribution for Messenger, enabling clients to verify the authenticity of HSM public keys and ensuring secure data storage. This is complemented by the publication of evidence on fleet deployments, providing transparency and proof of secure operations. The system, utilizing tamper-resistant hardware security modules (HSMs), ensures that users' backups remain inaccessible to Meta and third-party providers.

SocialScale
1 min
EngineeringApr 21, 2026

Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge

Meta engineers modernized the Facebook Groups Search to improve content discovery, consumption, and validation by adopting a hybrid retrieval architecture and automated model-based evaluation. The new framework combines the precision of inverted indices with the conceptual understanding of dense vector representations, yielding tangible improvements in search engagement and relevance. This modernization addresses the major friction points people experience when searching community content, enabling users to more reliably discover and validate community knowledge.

SocialScale
1 min
EngineeringApr 16, 2026

Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale

Meta's Capacity Efficiency Program utilizes a unified AI agent platform to automate optimization of performance issues in their infrastructure, freeing up engineers' time to innovate on new products. The platform encodes domain expertise of senior engineers into reusable skills, reducing power consumption by hundreds of megawatts and compressing hours-long manual regression investigations into minutes. By combining standardized tool interfaces with encoded domain expertise, AI agents can automate both finding and fixing performance issues, enabling the program to scale without proportionally increasing headcount.

SocialScale
1 min
EngineeringApr 16, 2026

Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways

Here's a 2-3 sentence summary of the post on Post-Quantum Cryptography Migration at Meta: Meta has adopted a comprehensive post-quantum cryptography (PQC) migration strategy to protect against quantum attackers and safeguard sensitive information, aligning with industry standards and guidelines from NIST and NCSC. The strategy involves PQC Migration Levels, categorizing organizations from PQ-Aware to PQ-Enabled, to help teams assess and prepare for post-quantum cryptography. By implementing this approach, Meta aims to share practical guidance and accelerate the broader community's transition to a post-quantum future, minimizing risks and costs associated with quantum threats.

SocialScale
1 min
EngineeringApr 9, 2026

Escaping the Fork: How Meta Modernized WebRTC Across 50+ Use Cases

Here's a 3-sentence summary of the article: Meta's engineering team overcame the "forking trap" by modernizing WebRTC across 50+ use cases using a dual-stack architecture that enables safe A/B testing. They achieved this by building a shim layer that exposes a unified, version-agnostic API, allowing them to dispatch between legacy and latest WebRTC implementations at runtime. The solution involved automated renamespacing, bulk namespace imports, and template-based helper libraries to ensure backward compatibility, reducing binary size and symbol collisions, and allowing for continuous upgrade cycles of the library.

SocialScale
1 min
EngineeringApr 8, 2026

Trust But Canary: Configuration Safety at Scale

Meta's Configuration team uses "trust but canary" approach to ensure safe rollouts at scale, leveraging canarying and progressive rollouts to detect regressions early. Health checks and monitoring signals help catch issues before they impact users, while incident reviews focus on improving systems rather than assigning blame. Additionally, data and AI/machine learning tools reduce alert noise and speed up troubleshooting.

SocialScale
1 min
EngineeringApr 6, 2026

How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines

Meta engineers developed an AI-powered system to map tribal knowledge in their large-scale data pipelines, which span four repositories and over 4,100 files. They created a pre-compute engine with 50+ AI agents that systematically read every file and generated 59 concise context files, resulting in AI agents having structured navigation guides for 100% of their code modules. This led to a 40% reduction in AI agent tool calls per task and improved quality, with scores increasing from 3.65 to 4.20 out of 5.0.

SocialScale
1 min
EngineeringApr 2, 2026

KernelEvolve: How Meta’s Ranking Engineer Agent Optimizes AI Infrastructure

Here's a concise summary of the article in 2-3 sentences: KernelEvolve is an agentic kernel authoring system developed by Meta to optimize low-level hardware kernels for heterogeneous AI accelerators. It automatically generates and optimizes production-grade kernels for various hardware platforms, including NVIDIA GPUs, AMD GPUs, Meta's custom MTIA silicon chips, and CPUs, improving inference throughput by 60% and training throughput by 25%. KernelEvolve treats kernel optimization as a search problem, exploring hundreds of alternative kernel implementations to identify a solution that often matches or exceeds human expert performance.

SocialScale
1 min
EngineeringMar 31, 2026

Meta Adaptive Ranking Model: Bending the Inference Scaling Curve to Serve LLM-Scale Models for Ads

Meta's engineers have designed the Meta Adaptive Ranking Model to efficiently serve Large Language Model (LLM)-scale Ads Recommender runtime models. This is achieved through three key innovations: Inference-Efficient Model Scaling, Model/System Co-Design, and Reimagined Serving Infrastructure. The Adaptive Ranking Model bends the inference scaling curve, maintaining sub-second latency and reducing costs while increasing model complexity. The Meta Adaptive Ranking Model uses Request-Oriented Optimization and Request-Oriented Sequence Scaling to minimize computational redundancy and optimize storage footprints, significantly reducing costs. Additionally, the Wukong Turbo architecture introduces refinements to improve throughput, stability, and efficiency, enabling the use of LLM-scale models without compromising latency or cost efficiency. By leveraging these innovations, the Meta Adaptive Ranking Model delivers a +3% increase in ad conversions and a +5% increase in ad click-through rate for targeted users.

SocialScale
1 min
EngineeringMar 30, 2026

AI for American-Produced Cement and Concrete

Meta has released an AI model called Bayesian Optimization for Concrete (BOxCrete) to design and optimize concrete mixes, improving quality and sustainability. The model allows U.S.-based concrete producers to rapidly explore and validate new formulations without months in the lab, incorporating more U.S.-made materials into their mixes. BOxCrete has already shown real-world impact, improving concrete mixes used in Meta's data center in Rosemount, MN by 43% in structural strength and reducing cracking risk by 10%.

SocialScale
1 min
EngineeringMar 18, 2026

Friend Bubbles: Enhancing Social Discovery on Facebook Reels

Facebook's Friend Bubbles feature enhances social discovery on Facebook Reels by showcasing content liked or reacted to by a user's friends, making it easier to connect over shared interests. The system uses machine learning to estimate relationship strength and rank content, combining social and interest signals to recommend personalized content. This feature creates a feedback loop that improves recommendations and strengthens social connections, fostering meaningful engagement and conversation. The Friend Bubbles recommendation system includes components such as Viewer-Friend Closeness and Video Relevance, which work together to surface relevant, friend-interacted content. The system uses complementary machine learning models to identify which connections a person feels closest to, and incorporates context-specific closeness prediction models to capture closeness in context. To rank high-quality friend content effectively, the system sources inventory by expanding the top of the funnel to ensure sufficient candidate volume, and enables models to incorporate viewer-friend relationship strength and learn downstream engagement on videos with social bubbles. A continuous feedback loop ensures that the system improves its understanding of

SocialScale
1 min
EngineeringMar 17, 2026

Ranking Engineer Agent (REA): The Autonomous AI Agent Accelerating Meta’s Ads Ranking Innovation

Meta's Ranking Engineer Agent (REA) is an autonomous AI agent that automates the end-to-end machine learning (ML) lifecycle for ads ranking models, reducing the need for manual intervention in ML experimentation. REA executes key tasks such as hypothesis generation, training job launching, debugging, and iteration, and has delivered 2x model accuracy and 5x engineering output in its first production rollout. REA addresses the challenges of long-horizon, asynchronous workflows through its hibernate-and-wake mechanism, and high-quality, diverse hypothesis generation through its dual-source hypothesis engine and three-phase planning framework.

SocialScale
1 min
EngineeringMar 13, 2026

Patch Me If You Can: AI Codemods for Secure-by-Default Android Apps

Even seemingly simple engineering tasks — like updating an API — can become monumental undertakings when you’re dealing with millions of lines of code...

SocialScale
1 min
EngineeringMar 9, 2026

How Advanced Browsing Protection Works in Messenger

We’re sharing the technical details behind how Advanced Browsing Protection (ABP) in Messenger protects the privacy of the links clicked on within chats while...

SocialScale
1 min
EngineeringMar 2, 2026

FFmpeg at Meta: Media Processing at Scale

FFmpeg is truly a multi-tool for media processing. As an industry-standard tool it supports a wide variety of audio and video codecs and container formats. It...

SocialScale
1 min