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 Engineer Agent’s ML exploration capability, which autonomously designs, executes, and analyzes ranking model experiments. This post covers how to optimize the low-level infrastructure that makes those models run efficiently at scale. We introduce KernelEvolve, an agentic kernel authoring system used by Ranking Engineer Agent and generally applicable to a range of AI models beyond Ads Ranking.
Summary
Meta operates a large fleet of heterogeneous hardware — NVIDIA GPUs, AMD GPUs, Meta’s custom MTIA silicon chips, and CPUs. Using this hardware effectively and efficiently requires developing software that translates high-level model operations into efficient, chip-specific instructions called optimized kernels. Authoring and optimizing kernels must be done for each new chip generation and ML model architecture. Beyond standard kernel operators like general matrix multiplications (GEMMs) and convolutions covered by vendor libraries, production workloads require many custom operators across ranking models. With the number of models and number of hardware types and generations, hand-tuning by kernel experts doesn’t scale.
