Declarative programming paradigms from logic programming and relational algebra to deep probabilistic programming gain their expressiveness from nondeterminism, but efficient evaluation of even theoretically tractable nondeterministic programs poses formidable engineering challenges, especially when the language is embedded as a domain-specific language in a general-purpose higher-order language like Python or Haskell. We aim to show that array broadcasting, an underappreciated semantic feature of modern embedded array programming systems like PyTorch and JAX, makes it possible to convert these systems into powerful, general and easy-to-use optimizing compiler toolchains for embedded nondeterministic DSLs.