[bitL]

ML engineer is a super boring job content-wise and has insane outside pressure. It's about building data pipelines, the ugly grunt work. ML/Data Scientist is the interesting job. Usually Data Scientists view ML Engineers as replaceable drones that don't understand anything interesting and do the boring part of the job for 2-3x less than they do. The only advantage of ML Engineers is that AutoML is unlikely going to replace some dirty work but might endanger outdated Data Scientists.

[nilkn]

I might disagree on this. The software engineering behind production machine learning systems can be quite interesting and nontrivial. It really depends on the scope of the challenges being faced. If you have thousands of models that need to be served in production and continually retrained and monitored, that becomes a pretty sophisticated problem space to work in.

[bitL]

Yes, however most ML engineers don't get to work at Jeff Dean's level to actually do such interesting work. There are very few companies willing to write their own Horovod or distributed PyTorch.

[whymauri]

Except for the part where data science is an incredibly broad term and the majority of the positions are seemingly what used to be called 'data analyst'.

It makes finding good positions really hard.

[anon44]

the vast majority of ML/data scientist positions are actually similar to the hypothetical ML engineer position you described.