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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 Trust But Canary: Configuration Safety at Scale 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
Patch Me If You Can: AI Codemods for Secure-by-Default An...
2026-03-13 · via Engineering at Meta

Even seemingly simple engineering tasks — like updating an API — can become monumental undertakings when you’re dealing with millions of lines of code and thousands of engineers, especially if the changes are security-related. Nowhere is this more apparent than in mobile security, where a single class of vulnerability can be replicated across hundreds of call sites scattered throughout a sprawling, multi-app codebase serving billions of users.

Meta’s Product Security team has developed a two-pronged strategy to address this:

  • Designing secure-by-default frameworks that wrap potentially unsafe Android OS APIs and make the secure path the easiest path for developers, and
  • Leveraging generative AI to automate the migration of existing code to those frameworks at scale.

The result is a system that can propose, validate, and submit security patches across millions of lines of code with minimal friction for the engineers who own them.

On this episode of the Meta Tech Podcast, Pascal Hartig talks to Alex and Tanu, from Meta’s Product Security team about the challenges and learnings from the journey of making Meta’s mobile frameworks more secure at a scale few companies ever experience. Tune in to this episode and join us as we explore the compelling crossroads of security, automation, and AI within mobile development.

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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.

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