Search-based techniques have been demonstrated effective in solving complex optimization problems that arise in domain-specific compilers for machine learning (ML). Unfortunately, deploying such techniques in production compilers is impeded by several limitations. In this talk, I will present an autotuner for production ML compilers that can tune both graph-level and subgraph-level optimizations at multiple compilation stages. The autotuner applies a flexible search methodology that defines a search formulation for joint optimizations by accurately modeling the interactions between different compiler passes. The autotuner tunes tensor layouts, operator fusion decisions, tile sizes, and code generation parameters in XLA, a production ML compiler, using various search strategies. We demonstrate how to incorporate machine learning techniques such as a learned cost model and various learning-based search strategies to reduce autotuning time. Our learned cost model has high accuracy and outperforms a heavily-optimized analytical performance model. In an evaluation across 150 ML training and inference models on Tensor Processing Units (TPUs), the autotuner offers up to 2.4x and an average 5% runtime speedup over the heavily-optimized XLA compiler. The autotuner has been deployed to automatically tune the most heavily-used production models in Google’s fleet everyday.