How do you optimize CI/CD pipeline performance at scale with distributed caching and remote build execution?

At scale (hundreds of developers, thousands of pipeline runs per day), pipeline performance requires systematic optimization. Distributed caching: tools like Bazel and Nx maintain a content-addressable cache keyed by inputs — if nothing in the dependency graph changed, the cached output is used instead of rebuilding. With a remote cache (NFS, GCS, S3), all developers and CI runners share the same cache, meaning one developer's build primes the cache for everyone. Remote build execution: Bazel's Remote Execution API (RBE) sends individual build and test actions to a pool of specialized workers (via BuildFarm or EngFlow), parallelizing work across many machines simultaneously — a build that takes 30 minutes locally takes 3 minutes with 10-way parallelism. Test result caching: skip re-running tests whose inputs (code, data) have not changed since the last passing run. Incremental Docker builds: use BuildKit's cache mounts and --cache-from to share layer caches between builds. Google's internal build system runs millions of build and test actions per day with sub-minute feedback using these techniques.