I’m currently a first-year Master’s student in Data Science & AI, and I’m trying to figure out whether a research-oriented career is right for me.
So far, most of my experience has been in applied machine learning. None of my internships were formally titled “ML Engineer” or “ML Research,” but the work itself was pretty applied ML-heavy. During undergrad, I was also part of a research lab where I worked on a computer vision project for manufacturing. That mostly involved training a CV model, and it led to a paper at a manufacturing conference.
The issue is that I don’t feel like I’ve been deeply exposed to what “real” research looks like, especially at places like AI labs (OpenAI, Anthropic, Amazon AGI, Meta FAIR, or similar). At a glance, they seem to be doing some cool work but wouldn't we all like to work for them lol. Because of that, I’m struggling to tell whether I’d actually enjoy or succeed in a research-oriented role.
A few things I’m trying to figure out:
- How do you know if research is a good fit for you without having had much exposure to it?
- Is it too late to pivot toward research if you didn’t fully commit to it during undergrad or half-way through masters?
- Are side projects a good way to explore research, or do they not really capture what research work is like?
- Should I be actively trying to get a research opportunity now, even though I already have an applied data science internship lined up for this summer?
I’ve also thought about doing a PhD, but I don't think its in the cards for me. Committing 5+ years feels like a big decision, and from what I understand, it requires a strong intrinsic motivation for research that I’m not sure I have yet.
Would really appreciate any advice from people who’ve been in a similar position or have experience in both applied ML and research paths.
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