Multidimensional array operations are ubiquitous in machine learning. The dominant ecosystem in this field is centred around Python and NumPy, where programs are expressed with elaborate and error-prone API calls acting on entire arrays, which derives from APL. Such code is difficult to statically analyse and maintain. Various other array programming paradigms offer to solve these problems, in particular the pointful approach of Dex. However, only limited solutions based on Einstein summation have been embedded in Python. We describe Ein, a proof-of-concept pointful array DSL embedded in Python, which generates performant and type-safe calls to NumPy with potential for further analysis and optimisations. Ein reconciles the readability of comprehension-style definitions with the capabilities of existing frameworks.