- You can think about roles on a spectrum -\
  ML Researcher -&gt; Applied Scientist -&gt; ML Engineer -&gt; ML Infrastructure Engineer\
  You go in decreasing of ML knowledge and increasing order of software skills. So one consideration would be thinking about where you want to land ultimately.
- Most ML infrastructure overlaps a lot with a typical backend role. So you would face the same set of challenges more often than not. At the same time, there will be a lot of new ML design patterns - distillation, multi-stage models, etc. But you can easily pick them up with basic ML background. So I would say the transition is possible if you are interested, as Alex said before.
- It would be a good idea to start as an ML Infrastructure engineer and if you like the modeling parts of it, look to contribute towards that in a project working with MLEs. First, consider the problems that interest you - recommendation, ads ranking/auctions, graphs, natural language processing, computer vision, etc. Then talk with teams in those areas and see if there is a match.

I hope this helps :)

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