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Engineering at Meta

ZGateway: Learnings from Putting a Proxy in Front of ZippyDB An Organizational Second Brain: Building an AI That Learns From Experts MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model Exploring Hierarchical Interest Representation For Meta Ads Deep Funnel Optimization Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler Meta’s AI Storage Blueprint at Scale 10 Years of Meta’s Commitment to Python Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study How Meta Engineered Ultra-Narrow Batteries for AI Glasses Adopting AV1 for Real-Time Communication (RTC) at Scale Lights Out, Systems On: Validating Instant Power Loss Readiness SilverTorch: Index as Model — A New Retrieval Paradigm for Recommendation Systems Reel Friends: Building Social Discovery that Scales to Billions Migrating Data Ingestion Systems at Meta Scale Labyrinth 1.1: Making End-to-End Encrypted Backups Even More Reliable How Meta Is Strengthening End-to-End Encrypted Backups Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge Capacity Efficiency at Meta: How Unified AI Agents Optimize Performance at Hyperscale Post-Quantum Cryptography Migration at Meta: Framework, Lessons, and Takeaways Escaping the Fork: How Meta Modernized WebRTC Across 50+ Use Cases How Meta Used AI to Map Tribal Knowledge in Large-Scale Data Pipelines KernelEvolve: How Meta’s Ranking Engineer Agent Optimizes AI Infrastructure Meta Adaptive Ranking Model: Bending the Inference Scaling Curve to Serve LLM-Scale Models for Ads AI for American-Produced Cement and Concrete Friend Bubbles: Enhancing Social Discovery on Facebook Reels Ranking Engineer Agent (REA): The Autonomous AI Agent Accelerating Meta’s Ads Ranking Innovation
Trust But Canary: Configuration Safety at Scale
By Pascal Hartig · 2026-04-09 · via Engineering at Meta

As AI increases developer speed and productivity it also increases the need for safeguards.

On this episode of the Meta Tech Podcast, Pascal Hartig sits down with Ishwari and Joe from Meta’s Configurations team to discuss how Meta makes config rollouts safe at scale. Listen in to learn about canarying and progressive rollouts, the health checks and monitoring signals used to catch regressions early, and how incident reviews focus on improving systems rather than blaming people.

They also talk about how data and AI/machine learning are slashing alert noise and speeding up bisecting when something goes wrong.

Download or listen to the episode below:

You can also find the episode wherever you get your podcasts, including:

The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.

Send us feedback on InstagramThreads, or X.

And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.

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