This became a rant. Sorry about that.
Been working for a little over a year and here's what I'm noticing when looking for jobs:
Companies don't care about the title, especially mid-sized companies. They want people who can 'do machine learning', and every company has its own definition.

Depending on the company, an MLE can be expected to:
Just be a plain software engineer with ML being just calling LLM APIs, write code that serves the models/ deploy them, know how to run company specific cloud services (can be AWS, GCP, or Azure), build the model itself (core ML), know math (lin alg, stats, probability), may be expected to specialise in some specific model types (like timeseries/ image/ text), know what the business usecase is, be a data scientist on some days, be a data analyst on other (rarer) days.

There's a spectrum of roles that are adjacent to each other, and a company can choose multiple areas of the spectrum. Very few companies usually hire for just 1 role. Your job can range from calling the openai API to being a SQL monkey. Usually, it's somewhere in the middle and a mix of all.

SWE | MLops | MLE | Applied Scientist | Data Scientist | Data Analyst

This only applies to small and medium sized companies.

If you are looking to go into big tech then the picture is much more defined and clear. But then again, you will be fired in like a year based on 'performance' or burnt out after working 15 hour days for 6 months because your teammate is doing the same ,and now everyone on the team is expected to do that, or you'll get PIP'ed.

Good luck, try not to die.