I recently earned my PhD in applied machine learning with focus on NLP. Although I've started my career with a title of "research engineer," my role leans heavily towards research rather than engineering. I've noticed that most job opportunities where I'm considered a strong candidate tend to be research-oriented. However, I don't envision myself in a long-term career focused on publishing papers; instead, I'm more interested in the ML engineering.
Could anyone share insights or advice on transitioning from a research-focused role to a more engineering-centric position in the field of machine learning? What steps should I take to make myself a more attractive candidate for engineering roles?
I find that a lot of people coming from research over-emphasize their research and down-play the engineering side of things that they also had to do in order to do meaningful research.
Anyway, all of this is a continuous spectrum. There are positions that are still closer to research than others but are engineering anyway. In my experience there are little jobs that require you to write papers all the time.
You should think about it like this: What is needed to bring an NLP algorithm to production and which parts of that do I know or don't know.