As a Data/ML Engineer, I cannot quite recommend this route: this role is unlikely to get you to a true ML role.
It will give you a ton of exposure to ML techniques and infrastructure practices, but the true modeling work is still done by PhDs with the prerequisite background/knowledge. You will be taking black boxes -- pre-built models -- and doing the data cleaning, fine-tuning, and experimentation.
I know a handful of people who transitioned to full-on ML from this role, but those individuals were quite gifted and hardworking, and probably could have learned without this exposure. There's a significant gap in knowledge that can only be attained through academic experience.
Or through a free online course like deeplearning.ai or fast.ai, and a few personal projects.
So many PhDs I've worked with have known the literature but couldn't produce something valuable to save their life. That might have been the nature of them being over billed on projects and spread too thin, but I do not think that is a requisite for building models. It certainly isn't for the rest of the engineering pipeline.
It depends on what company you're going for. If you are looking for a role in cutting-edge ML (FAANG/OpenAI/Deepmind/etc), the stuff you learn from these online courses does not provide the theoretical rigor required.
If you want a role in a small company building best-effort, out-of-the-box models, then the courses are plenty fine.
It will give you a ton of exposure to ML techniques and infrastructure practices, but the true modeling work is still done by PhDs with the prerequisite background/knowledge. You will be taking black boxes -- pre-built models -- and doing the data cleaning, fine-tuning, and experimentation.
I know a handful of people who transitioned to full-on ML from this role, but those individuals were quite gifted and hardworking, and probably could have learned without this exposure. There's a significant gap in knowledge that can only be attained through academic experience.