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In the probabilistic-programming paradigm, the application logic is specified by means of a description of a probabilistic model (by stating how a sample is being produced) using a Probabilistic Programming Language (PPL). The principal value one obtains from a probabilistic program lies in the inference thereof, that is, reasoning about the entire probability distribution that the program defines (e.g., finding a likely event or estimating its marginal probability). The PPAML kickoff meeting highlighted several research challenges regarding the development of inference infrastructure for PPL, for both increasing software efficiency and reducing software complexity, towards the goal of broadening the PPL applications and the community of implementers and programmers. These challenges include the design of an Application Program Interface (API), or alternatively an Intermediate Representation Language (IRL), that would allow new solvers to be plugged into existing PPLs, and for PPL engines to be able to pick from and combine solvers for a given problem.