I think you underestimate the level of software architecture and engineering skill that people with formal training in graduate level statistics bring to these jobs.
I manage a team of machine learning engineers in a mid-size ecommerce company and I can tell you that the same person who is optimizing Dockerfiles for better layer reuse & figuring out how our CI pipeline will safely get secrets needed to retrieve model files for integration tests is the same person researching new variations of triplet loss in a paper from arxiv they present in our team’s journal club & developing Bayesian hierarchical regression models and explaining why partial pooling using an industry-specialized prior distribution actually produces coefficients in the model fit that have a meaningful improvement over OLS for some business outcome.
The same people that are refactoring a collection of unit tests to be parallelizable and save us 45 seconds on every test run are also running huge hyperparameter tuning experiments to get a learning rate scheduler that allows us to reduce training time for an in-house deep GRU neural network from 24 hours to 15 hours, and they are defining infrastructure as code tooling to even create the very GPU environments where this training is taking place.
The skill set really is extremely different from general backend engineer with a proclivity for hacking around ML models and also is really different from “research developer” who rarely deals with end to end systems or necessary concerns of production or quality code factoring, and also is super different from data science which is effectively just ad hoc business analytics but with more impressive pedigree on the resume.
I’d define a machine learning engineer as taking someone with several years of graduate experience in statistics / machine learning inclusive of formal probability theory, analysis, topology, Bayesian stats (or work + research experience that is equivalent, though it’s super rare for this to be able to replace formal graduate math training), and then adding senior level skill set in high-performance computing, generalist backend engineering, system architecture, and full management of the lifecycle of complex production systems.
The only piece of common modern engineering that I would say ML engineers typically don’t have a senior-ish level of command over is frontend development (though some do out of just hobbyist interest).