Engineering at Meta1 min readApr 2, 2026

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

This is the second post in the Ranking Engineer Agent blog series exploring the autonomous AI capabilities accelerating Meta’s Ads Ranking innovation. The previous post introduced Ranking Engine

Cloud Performance AI Distributed Systems

KubernetesRanking EngineMeta AI InfrastructureAI InfrastructureAutonomous Systems

AI Summary

Meta engineers introduce KernelEvolve, an agentic kernel authoring system that optimizes AI model performance on various hardware platforms, including NVIDIA GPUs, AMD GPUs, and Meta's custom MTIA silicon chips. KernelEvolve uses automated search to find optimized performance, reducing development time from weeks to hours and yielding improvements of up to 60% in inference throughput. By treating kernel optimization as a search problem, KernelEvolve explores hundreds of alternative kernel implementations, often matching or exceeding human expert performance in hours rather than weeks. This automation enables Meta's production environment to serve trillions of daily inference requests while reducing engineering effort required to integrate heterogeneous hardware for training and inference. KernelEvolve addresses the challenge of explosive kernel growth by autonomously generating and optimizing production-grade kernels for complex AI models and diverse hardware, adapting to model and hardware evolution, and enabling Meta to rapidly integrate new hardware into its AI infrastructure.

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

Meta engineers developed a pre-compute engine with a swarm of AI agents to generate 59 concise context files encoding tribal knowledge that aids in navigating a large-scale data processing pipeline. This resulted in AI agents having structured navigation guides for 100% of the code modules (up from 5%) and documenting 50+ non-obvious patterns. The system works with leading models, is model-agnostic, and self-maintaining, with automated jobs periodically validating file paths and updating context.

AIDistributed SystemsPerformance

AIMetaData PipelinesAPI DesignKnowledge ManagementLarge-Scale SystemsMachine Learning

Engineering at Meta1 min6d ago

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

Meta's Engineering team developed the Adaptive Ranking Model to serve Large Language Model (LLM)-scale models in real-time ads recommendation environments. This innovation bends the inference scaling curve, balancing increasing model complexity with low latency and cost efficiency. By dynamically aligning model complexity with user context and intent, the Adaptive Ranking Model ensures a high-quality ad experience while maintaining strict, sub-second latency and improving advertiser value. The Adaptive Ranking Model achieves inference efficiency through three key innovations: Inference-Efficient Model Scaling, Model/System Co-Design, and Reimagined Serving Infrastructure. These innovations enable the system to serve LLM-scale models at sub-second latency, unlocking a deeper understanding of user interests and intent without compromising the experience. The system has delivered a +3% increase in ad conversions and +5% increase in ad click-through rate for targeted users since its launch on Instagram.

AIDistributed SystemsPerformance

LLMAI Recommendation Systems (RecSys)MetaModel ScalingInference Scaling CurveLarge Language Models (LLM-Scale Models)

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