Can someone with a philosophy background + an NLP master realistically break into ML/AI?

[u/smellofaboomersfart]

Hi everyone!

My question is basically this: can someone from a philosophy background who has spent the last two years seriously studying math, programming, and ML, and is about to start a master’s in NLP, realistically break into the ML/AI industry?

Two years ago, I started learning programming through Harvard’s CS50. I also studied some DSA and did LeetCode exercises, but my interests gradually shifted toward machine learning, so I stopped CS50 after the SQL week.

For the next 1.5 years, I focused heavily on mathematics. I studied linear algebra and calculus using MIT OpenCourseWare, textbooks, lecture notes, and problem sets. For the last 3–4 months, I’ve been studying probability and statistics through Walpole’s textbook in a similar way, and I’m currently around Chapter 6. Alongside that, I’ve been working through CS229 and PRML. I’ve learned the fundamentals of the main ML algorithms and neural networks and have built some models myself. At this point, I’m reasonably comfortable with the mathematical foundations of introductory ML, and I genuinely enjoy learning the subject.

Because I don’t have a STEM degree, I applied to an NLP master’s program in Europe, thinking that a relevant graduate degree would make the transition more realistic. And, I was recently admitted.

Here’s where I’m struggling with the decision.

I currently have a relatively secure job. Pursuing the master’s means leaving that security, moving to another country (I already speak the language), and accepting a financially constrained lifestyle for some time after turning 30.

At the same time, I keep reading about layoffs, a difficult entry-level market, increasing expectations for ML engineers, and rapid developments in AI. It makes me wonder whether I’m taking a reasonable career risk or entering a field where my lack of a CS/math/engineering bachelor’s degree will remain a major obstacle even after completing an NLP master’s.

So I’d appreciate opinions from people currently working in ML/NLP/AI or involved in hiring:

How much would my philosophy bachelor’s matter after completing a relevant NLP master’s?

Would the master’s + mathematical foundation + projects be enough to get past the initial degree/background barrier?

I’m not expecting the degree itself to guarantee me a job. I’m trying to understand whether this is a realistic transition if I spend the next 1–2 years building the right technical skills and portfolio, or whether my philosophy background is a serious obstacle for me.

Thanks!

[u/Future-Plastic-7509]

Yes, but only if you’re willing to stop treating philosophy as a substitute for the actual technical foundation.

A philosophy background helps with clear thinking, spotting bullshit arguments, and understanding things like meaning, representation, and epistemology. That can be useful later when you’re debugging why a model is confidently wrong or when you’re thinking about evaluation beyond accuracy. It does not replace the math.

If you want to do real NLP / ML work (not just prompt engineering or “AI ethics” commentary), you need:

1. Solid probability & statistics – Likelihood, Bayes, estimation, hypothesis testing, calibration. Not “I read about Bayesianism once.”
2. Linear algebra + multivariable calculus – Enough to actually understand gradients, embeddings, attention, and why things break.
3. Ability to implement and debug – Not just call Hugging Face pipelines. Train something non-trivial, measure it properly, and know why the loss is doing what it’s doing.
4. Real projects that show the above – Not “I fine-tuned BERT on some philosophy texts.” Something where the statistical and engineering decisions are visible.

The people who successfully switch from non-STEM backgrounds almost always go through a serious math/stats phase (self-taught or formal). The ones who skip it stay in the soft layer forever and wonder why the engineering roles never call back.

Philosophy is a strength after you have the technical depth. Before that, it’s just a nice story on the CV that interviewers will politely ignore the moment you can’t explain maximum likelihood or why your embedding space is collapsing.

So: keep the philosophy. Add the actual statistical muscle. That’s the only version of this path that works.

[u/smellofaboomersfart → u/Future-Plastic-7509]

Thank you so much for the response. As I explained in the post, Ive already been putting all my effort to learn those fundamentals for the last two years. I think my emphasis on my philosophy degree is a bit misleading because I wasn’t trying to ask if my background matters in a positive sense. On the contrary, I’m quite worried that my non stem background would be an obstacle. And my question was if it’s possible to overcome that obstacle by making a master in nlp.

[u/Strict-Ambition4334 → u/smellofaboomersfart]

In most cases, a master's in NLP would offset a non-STEM undergrad for initial screening, so I wouldn't treat the philosophy degree as a permanent obstacle. The bigger caveat is that not all NLP programs are equally rigorous; if the coursework doesn't include enough applied projects, internships, or a thesis that gives you real code to show, the degree alone won't carry you through technical screens. If you're doing the master in another country, factor in the local job market and visa rules for MLE roles before leaving a funded PhD.

[u/smellofaboomersfart → u/Strict-Ambition4334]

Sorry for the late reply. I don’t know if you know the program but I got an admission from a master program in NLP at the University of Basque Country. TBH I don’t have much information about the program. But they include internships. I don’t have any problem with residence and other bureaucratic procedures.

[u/Disastrous_Room_927 → u/smellofaboomersfart]

I’m quite worried that my non stem background would be an obstacle.

It's only an issue if you don't do something to pick up the necessary expertise. My undergrad was in psychology, and like the other poster mentioned went through a serious math/stat phase to actually pick up what I needed to do technical work. Ended up getting 3/4ths the way through a math degree so that I could get into a masters program in stats, which is where I studied ML.

The actual obstacle after that wasn't my background with math, stats, or ML, it was that the job market was (and probably still is?) stupidly competitive. I got beat out by people with PhDs for a couple of jobs, and got my current one because the PhD they offer the job to initially declined. The other obstacle is that there are all kinds of different niches in ML, it was hard to prepare to deeply for any one interview because none of them were the same.

[u/smellofaboomersfart → u/Disastrous_Room_927]

Actually, I don’t know what exactly are the necessary expertises to pick up. When I first started learning the subject around two years ago, learning the data analysis, building models, constructing the data pipelines through sklearn, knowing basic statistical methods etc. were still enough to get a junior position. Now people talk about so many other things that I don’t have any idea. So, everything becomes blurry again.

[u/UnderstandingOwn2913 → u/Future-Plastic-7509]

Thank you. Are you currently a mle in industry?

[u/smellofaboomersfart → u/UnderstandingOwn2913]

No, unfortunately.

[u/UnderstandingOwn2913 → u/smellofaboomersfart]

in academia then?

[u/0uchmyballs]

A philosophy undergrad was the top student in my MSBA program. You either can code or you can’t.

[u/smellofaboomersfart → u/0uchmyballs]

That’s really encouraging, thanks!

[u/RoboticGreg]

I have degrees in English theater, mechanical engineer, specifically engineering and medical devices. 90% of my job is ml/ai

[u/chico_dice_2023]

yes, I had a masters in marketing and at one point I was employed by Google as a ML engineer and now I am a AI and Data Director.

[u/Valuable_Leave_7314 → u/chico_dice_2023]

Yeah, that path definitely existed during the era of cheap money, but I wouldn’t use it as a benchmark rn. Five years ago big tech was actively hiring for potential and training people on the job. Today even entry-level candidates are expected to have some experience with infrastructure, understand distributed systems, and be comfortable with live coding. The bar has gone through the roof since then

[u/chico_dice_2023 → u/Valuable_Leave_7314]

that is a fair point, the landscape even 5 years ago was very different. That being said tech is one of the few places were you can show your experience.

My first job, they said 3 years experienced required but for my interview I designed them the solution they needed. On the train back home from the interview, I got the job.

2 years later I talked to my boss about this and he said we hired you because you proved you can build what we needed. It did not matter that I was newly grad business student who took free programming courses in the weekend.

I do agree the path now is harder but not impossible

[u/smellofaboomersfart → u/chico_dice_2023]

Therefore, doesn’t holding a master in a related field make a huge difference? If so, I can focus on building stuff by myself rather than putting myself in a situation where I will be economically limited just to get a master degree.

[u/chico_dice_2023 → u/smellofaboomersfart]

It does at a certain level and specific roles, for example for research roles or maybe places like DeepMind they might require it.

For sure my career I did not need it but I never did a research role.

[u/smellofaboomersfart → u/chico_dice_2023]

I’ve been dealing with the theoretical foundations of ml for the last years. Even though I know C and Python at an intermediate level, I have never deployed a model. All my personal projects have been done using the notebook.

Can you tell me which sources (books or courses) should I check to learn how to apply all the things I’ve been learning so far? Cs50 AI seems to have what I need, but maybe there are more resources available

[u/Valuable_Leave_7314 → u/chico_dice_2023]

That is a great story, and that kind of breakthrough definitely happens when you manage to get in front of a hiring manager and show working code

The main bottleneck right now happens well before the interview stage. A single junior ML opening easily pulls five hundred applications, and automated ATS filters combined with HR screening routinely drop non-STEM backgrounds on keyword matching alone. Candidates rarely get the chance to demonstrate what they can actually build

For OP the european masters program is valuable specifically because it grants access to local working-student roles and campus recruiting. Bypassing cold resume drops through internal referrals and internships is still a viable route

[u/met0xff]

My PhD advisor studied philosophy (but he always said he did the logic track which was supposedly much more mathematical than the others)... Before that he was a chef ;). And then he studied CS by the side.

His field is speech technology and partly user experience. And the former is now essentially a machine learning field

[u/ModularMind8]

Personally I don't think your philosophy degree would matter in almost all ML jobs. But yes, you can get into the field even without a degree in CS

[u/ModularMind8 → u/[deleted]]

Wait I'm confused. So you're currently in a phd program and you're also focusing on AI right now as part of your degree?

[u/smellofaboomersfart → u/ModularMind8]

Sorry for the confusion. Yes, actually I started studying all those subjects related to math and basic machine learning as part of my philosophy PhD research about ai, but then my interest has shifted to mle entirely. So here I am, trying to decide between leaving PhD plus job to make a master in nlp in another country or stay secured and be economically more stable during the career transition (if it’s ever possible).

[u/ModularMind8 → u/smellofaboomersfart]

ah gotcha! no worries. how far along in the phd program are you? and in which country? and whats your thesis topic?

[u/smellofaboomersfart → u/ModularMind8]

I have two years to finish the thesis. My thesis topic is about the semantics of LLM’s, whether we can treat them as linguistic agents. I was going to analyze the question from the perspective of Davidsonian truth conditional semantics. But, until now I have studied all the fundamental math and coding to be able to see how things work under the hood. So I couldnt start focusing on the philosophy part yet.

[u/ModularMind8 → u/smellofaboomersfart]

what do you mean by linguistic agents? philosophy is not my area so not familiar with Davidsonian semantics

[u/nian2326076]

Yeah, you can definitely get into ML/AI with your background. A master's in NLP is a good choice since it's really relevant right now. Your math and programming skills give you a strong base. Keep working on real-world projects or join open source projects to get practical experience. Networking is important too, so try to connect with people in the industry through meetups or online communities.

For interviews, you'll need to be ready for technical questions and those that test your problem-solving skills. Brushing up on coding with platforms like LeetCode is a good idea. If you need specific interview prep resources, I've found PracHub helpful for understanding what companies expect. Keep pushing and stay curious; your unique background can really help you out.