I am a CompSci senior focusing on ML. My university does not have applied research in ML, so doing ML in school (classes/research) is pretty much a one-way ticket to the theory/algorithms side of academia.

Last year, I had the epiphany that I am good at (and enjoy) solving problems by connecting components in a system instead of finagling a problem into a form where we can apply some mathematical law. Specifically, I have greatly enjoyed working with artists/UI/UX/frontend/non-tech people as their back-end counterpart. I have built data pipelines for MLEs, back-end for UI/UX/frontend designers, machine learning pipeline for BME researchers and projection/imagery software for artists.

I am pretty generalist and tool-agnostic, with more breadth than depth. That feels like software engineering.

That said, I do like to have an understanding of how things work and I have a decent tolerance for reading math. This is a really nerdy thing to say but I enjoyed deriving stuff like the convergence of gradient descent and I enjoyed real analysis. I also really enjoyed Nand2Tetris (open source course teaching you to build a minimal computer from NAND gates + compiler from OOP language to binary). It's extremely elegant to me, seeing the great design choices people made in the past. I feel like these are underappreciated in software engineering.

Right now, I have an opportunity to work with my RL professor, who has an amazing track record publishing at top conferences. I am really on the fence because his research is in RL algorithms and I had a very bad experience in my last algorithm research project somewhere else (I had a vague idea of what we were doing but nowhere near enough to make contributions). I am concurrently applying to jobs and Master's and I am pretty sure I will never touch this topic again if I go into industry after graduation.

I have these two questions: 1) Do I sound like the software engineers you know? What other roles do you think I am a good fit for? 2) Should I take this opportunity simply for research exposure? Do you think this is necessary in helping me keep up with trends as an applied practitioner in ML?

P.S. This is my first time posting on HN and this seems a lot longer than the average Ask HN post. I don't know if that's appropriate. Please lmk if I should go to a subreddit instead.

Thanks in advance if you read all that!