OCT 20, 2026
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Distributed Systems

Meta Engineering Blog

54 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

Engineering23h ago

GenRecs 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...

SocialScale
1 min
Engineering23h ago

The 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...

SocialScale
1 min
Engineering1d ago

Inside 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. The first phase...

SocialScale
1 min
Engineering4d ago

How Meta Built Forum With AI-Native Engineering

Meta’s newest app, Forum, is for anyone who wants to go deeper into the conversations in their Facebook groups. To build Forum, Meta engineers adopted a...

SocialScale
1 min
Engineering4d ago

2026 World Cup: Breaking Records, Spiky Charts and Reliability at Scale

Every four years, the World Cup becomes the largest planned stress test of our infrastructure. This year it also set an all-time record! There are a few...

SocialScale
1 min
Engineering4d ago

Sapienz Receives ISSTA 2026 Impact Paper Award

Ke Mao, Mark Harman, and Yue Jia have been recognized with the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) 2026 Most...

SocialScale
1 min
Engineering6d ago

Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI

Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model. GEM established Meta’s first LLM-inspired paradigm...

SocialScale
1 min
Engineering7d ago

OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks

At the 2026 Open Compute Project Global Summit, Meta is sharing the next generation of open networking infrastructure, including: Our first liquid-cooled...

SocialScale
1 min
Engineering7d ago

Shipping Media Over QUIC at Meta Scale for Instagram Live Ingest

Meta has begun deploying Media over QUIC (MoQ), an emerging open protocol for real-time video streaming at scale. MoQ will help simplify our protocol stack for...

SocialScale
1 min
Engineering7d ago

Turning Opportunistic AI Inference Capacity Into Better Ads Recommendations

At Meta, we’ve built a system that enhances ads recommendations by leveraging idle inference capacity in our fleet. Two elasticity levers in the system allow...

SocialScale
1 min
Engineering13d ago

NTS: Authenticated Time at Meta

Meta’s public time service now speaks NTS (Network Time Security, RFC 8915) at nts.meta.com. Packets are authenticated, so a device can verify the time came...

SocialScale
1 min
Engineering26d ago

Bringing Private Processing to Meta AI Glasses

We believe glasses are the best form factor for having AI help throughout your day. They can understand your personal context better than other kinds of...

SocialScale
1 min
Engineering28d ago

Open-Sourcing Rebalancer: A Generic, High-Performance Library for Solving Assignment Problems

We’re open-sourcing Rebalancer, the assignment-problem solver that has been used to solve resource allocation problems throughout Meta for over nine...

SocialScale
1 min
Engineering29d ago

Inside Petal: Building the World’s First Petabit-Class Transoceanic Subsea Cable

Petal, the next step in Meta’s subsea innovation, will be the first subsea cable to deliver petabit capacity at transoceanic distances, connecting France and...

SocialScale
1 min
EngineeringSep 3, 2026

ZGateway: Learnings from Putting a Proxy in Front of ZippyDB

We’re introducing ZGateway, the proxy we are using to unify traffic through ZippyDB, Meta’s most widely-used key value store. As a bonus, it also enables...

SocialScale
1 min
EngineeringSep 2, 2026

An Organizational Second Brain: Building an AI That Learns From Experts

We’ve built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an...

SocialScale
1 min
EngineeringAug 24, 2026

MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet

Training and serving frontier AI models depends on fast, reliable networks that move data between GPUs without wasting compute cycles. To meet this challenge...

SocialScale
1 min
EngineeringAug 24, 2026

MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines

MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models. We’re sharing...

SocialScale
1 min
EngineeringAug 12, 2026

How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees

Meta's WhatsApp implements Scam Alert, a user-controlled, optional feature using on-device machine learning to flag potential scam messages. The feature processes all data on-device, with no automatic reporting to WhatsApp or Meta, ensuring end-to-end encryption and user privacy. Scam Alert is trained on scam conversation patterns from user reports and uses differential privacy to anonymize and minimize data shared with WhatsApp. The feature's design adheres to core principles, including on-device only processing, no automatic reporting, and user control. Message content remains private, and telemetry is processed through a confidential computing environment to provide aggregate and anonymous counts to WhatsApp. The machine learning model is publicly published and verifiable, and users can trust or block chats to refine the feature's accuracy.

SocialScale
1 min
EngineeringAug 5, 2026

From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking

Here's a 2-3 sentence summary of the engineering blog post: Meta's Ads Recommendation Model has achieved a cumulative lift of 6% in conversions on Instagram, 3% in conversions on Facebook, and 3.5% in ad clicks on Facebook by introducing a multi-stage sequence model that decouples offline user modeling from online ranking tasks. The multi-stage model uses a transformer-based sequence model and a learning technique based on dense tokenization and target-aware attention to efficiently learn feature interactions directly from data. The architecture enables predictable scaling laws for ads recommendations, improving performance without proportional increases in serving resources, and provides a flexible production strategy for generalizing sequence learning models.

SocialScale
1 min