I think the concept of Machine Learning clashes in many ways with the core values of an engineer. Engineering is about constructing new things, making them correct by design. Machine learning, on the other hand, is throwing data at a problem, waiting for data to be processed, and hoping that the resulting models make sense. While both useful, it requires such a different mindset, that I wonder if engineers would feel satisfied working on such solutions.
Using machine learning is indeed more like science than engineering. After all it is just building a model based on data that you have seen. The advantage of using ML is the model can be arbitrarily complex and something you'd never be able to come up with manually. But that model is only a part of a larger project.
To the engineer it seems no different to designing a solution in the context of accepted scientific theories. You can't engineer the theories, they are accepted based on evidence. But you can build the project around it.
Engineering is about solving problems under constraints. ML is a tool in the tool box to use against problems when the constraints are appropriate, similar to just about any algorithm
ML in practice (from my xp) is mostly getting and cleaning data. As far as training, testing, and deploying a model, to an engineer it's just more algorithms.
While some algorithms may have more explainability than others, the engineer cares if they solve the business problem at hand.
The root of the word "engineer" means "to give birth to". In that sense, using Machine Learning to evolutionry give rise to new models is actually quite fitting.
