Today's "high productivity" programming languages such as Python lack the performance of harder-to-program "efficiency" languages (CUDA, Cilk, C with OpenMP) that can exploit extensive programmer knowledge of parallel hardware architectures. We combine efficiency-language performance with productivity-language programmability using selective embedded just-in-time specialization (SEJITS). At runtime, we specialize (generate, compile, and execute efficiency-language source code for) an application-specific and platform-specific subset of a productivity language, largely invisibly to the application programmer. Because the specialization machinery is implemented in the productivity language itself, it is easy for efficiency programmers to incrementally add specializers for new domain abstractions, new hardware, or both. SEJITS has the potential to bridge productivity-layer research and efficiency-layer research, allowing domain experts to exploit different parallel hardware architectures with a fraction of the programmer time and effort usually required.