Your comment struck because superciliously I agree, but when thinking about it, it's quite true. Both are strongly related but quite different, just as a marathon runner (though better than average) will not set world records on a 100m sprint.
A software engineer can easily use a XGB model and get some outputs, but is a long way from having a deep understanding of statistical distributions.
A ML engineer can easily put together a Django website, but is far from designing a maintainable software application.
Maybe on average it's true, but it's also a weird tone to strike in marketing materials. Where I work the MLE's are expected to write production level code and deploy their own services. We explicitly hire 1 level down on the SDE ladder when hiring MLEs, so a senior MLE should basically be almost a senior SDE.
A software engineer can easily use a XGB model and get some outputs, but is a long way from having a deep understanding of statistical distributions. A ML engineer can easily put together a Django website, but is far from designing a maintainable software application.