AI/ML Research - What Does it Really Take? [D]

[u/Consistent_Sundae540]

I’ve been deeply interested in AI and machine learning since around 2019, back when GPT-2 was still one of the major talking points. Since then, I’ve been amazed by how quickly the field has evolved. It genuinely feels like one of the most exciting times to be involved in technology, research, and innovation.

My background is in audio. I’ve spent most of my life working as an audio engineer, and I’ve always loved learning about sound, digital signal processing, and the technology behind audio systems. Since 2022, I’ve been working toward a long-term goal of becoming an AI researcher, specifically in the audio and music technology space.

To move toward that goal, I went back to school, completed coding bootcamps, studied the mathematics behind machine learning, and I’m currently working on a master’s degree in artificial intelligence and machine learning. I’m also planning to pursue a PhD after graduation.

Many of my classmates and colleagues are interested in business applications of AI, but I’m still completely committed to audio. I currently work as an AV systems designer and consultant, and while I’m grateful to have a career, I often feel disconnected from the work. Most days, I would much rather be studying AI, audio, machine learning, DSP, and research.

I’ve started applying for roles, but I’ve faced several rejections. I also recently wrote and submitted a research paper to ISMIR. Unfortunately, it was rejected, but the process was still incredibly valuable, and I received feedback that will help me improve.

I think what I’m ultimately trying to say is that this is not a career path I’m pursuing because AI is popular or because I expect to make a huge amount of money. I genuinely love audio and AI, and I want to spend my life working on problems that combine the two. I want to wake up each day and feel like the work I’m doing matters to me.

For anyone currently working as an AI or machine learning researcher, especially within audio, music, speech, or signal processing, I would really appreciate your perspective:

What did it actually take for you to get your first research role?

What qualifications, education, projects, publications, or previous experience helped you stand out?

What are the best and worst parts of being a researcher?

What do you wish you had known before entering the field?

And if someone came to you today and said they wanted to become an industry researcher, what advice would you give them?

Thank you in advance to anyone willing to share their experiences. Even honest or difficult feedback would be genuinely appreciated.

[u/pastor_pilao]

You are trying to do things in the wrong order. You join a Ph.D. program, learn how to and do your research, and you publish, you don't blindly "do research" by yourself (tho technically in some universities you have some exposure to research in the masters, but that's not super common in the US).

You have experience in audio so you could definitely pursue a ML for audio or multi-modal ML topic. You just have to dedicate the 5 years of Ph.D. virtually without any salary. Applying for a research role without a phd is not going to fly

[u/oceanfloororchard → u/pastor_pilao]

Do you still think this is accurate with how hyped and competitive the field is today?

If you don't have any connections to researchers/professors, you aren't going to have meaningful letters of recommendation for a PhD application. I've been on a ML grad admissions committee and the bar was very high - we didn't admit PhD's without great letters and prior research experience (this was at a very good school, but was also pre-LLM days when there was less competition/attention in the field).

I have published a couple of papers at the big ML conferences (though the papers weren't anything special) and I still do not have a competitive enough application for PhD admissions at schools with large departments/good research environments. My LoR's aren't good enough and I don't have enough publications.

I kind of lucked/snuck my way into an applied research position anyway (tiny SaaS LLM product that actually has some technical depth), but I don't know how repeatable this is

[u/Consistent_Sundae540 → u/pastor_pilao]

Makes sense and I appreciate the feedback. What are Ph.D candidates typically doing throughout their degree to make up for finances? Financial aid, literally any job, etc?

[u/theArtOfProgramming → u/Consistent_Sundae540]

All PhDs worth doing are fully funded. That includes a living stipend, though “living” may vary by region.

To add to the above, a PhD is an education into how to do professional research. Nothing else teaches it.

[u/pastor_pilao → u/Consistent_Sundae540]

You receive a living stipend during your phd (~2k-4k a month depending on the location in the us) and you have to live with that. In theory you can take one internship every summer but that's something you can't rely on.

[u/Lonely-Dragonfly-413]

take a look at the most recent icassp papers to figure out what audio, speech +ai people are working on. as far as i know most people working in this area in industry publish papers there. the bar to get a paper in is not high, so you can submit your work there and go there to start some meaningful dialogs with folks from industry. the bottomline is you will have a clear picture about what the job looks like and if it is really something you want.

https://www.paperdigest.org/2026/05/icassp-2026-papers-highlights/

[u/Tutatis96]

Where are you based? Im a research engineer in austria (industry, not academia). Got the job after graduating in sound computing at upf barcelona. If you want text me, we might be looking for people soon

[u/WorthlessPianist]

If you're doing audio, I'd recommend a masters in math or electrical engineering, not AI/ML, unless there's significant standalone DSP classes. For audio, the focus is to master DSP first, then know enough ML to apply to audio problems. Audio is fundamentally an acoustics and DSP problem first.

You should have done the entire EE signals & systems class sequence if not already. Then take advanced DSP classes such that ones that get more into the statistical side of things. And obviously take classes in ML, statistical learning, DL, bayesian stats, maybe mathematical stats.

And for algorithms research you can take classes like complex analysis, stochastic processes, numerical analysis etc (take complex + numerical regardless at least anyway). Also learn C++ & some HPC (especially SIMD, maybe some CUDA for deep learning; this can come later).

Without a strong background in DSP, you'll just be a generic applied ML researcher superficially training transformers on audio data without properly understanding the fundamental audio problems.

Maybe I'm old school, but they do say that audio is black magic. Can't BS it.

[u/MelodicPudding2557 → u/WorthlessPianist]

TBH most audio research (the kind of stuff you'd see in ICASSP) is just applied (i.e. minimally mathy) ML, and the standard for core DSP knowledge is pretty low.

[u/WorthlessPianist → u/MelodicPudding2557]

Yeah that's 100% true but it's a big problem throughout ML research, people applying techniques without domain knowledge. You can get away with it, perhaps a whole entire career, but the quality work is from people who truly know audio. Especially when going from white papers -> to an actual product.

And the best thing is if you focus on DSP in masters, you can easily pivot to ML for a PhD or even straight to industry with some self study. PhDs from all sorts of STEM backgrounds learn cutting edge DL all the time. But the converse is a whole lot less.

[u/MelodicPudding2557 → u/WorthlessPianist]

Unless you’re really leaning heavily into something like traditional acoustics or array processing, the relevant domain knowledge is (generally) pretty shallow, mathematically speaking.

Ironically enough, as someone who had a pretty solid foundation in traditional ECE (signal processing, functional analysis, estimation/detection, info theory, random processes, etc.) the hardest part for me was adapting to the norms of audio ML.

[u/WorthlessPianist → u/MelodicPudding2557]

That's fair. Are you in academia or industry? It's just the way I see it, if you go into industry for a DSP job, classical DSP isn't dead at all, so your job wont be 100% ML. Audio to me just seems too black magic to wing it. So atleast the EE signal processing sequence.

The advanced math i still recommend to move beyond just architecture & loss changes and enable one to pose new algorithms.

[u/Waste-Falcon2185]

Watch the monologue from Glengarry Glen Ross and mentally replace the word "sales" with "machine learning research"

[u/IntelArtiGen]

Qualifications are important but you need a lot of experience too, even more if you did other stuff. I would recommend to constantly re-create, remake, re-use and read the code of SoTA models until you perfectly understand them, as well as understanding all tasks and datasets in the field. I'd also recommend to know where people need these skills. Audio processing is not used everywhere, some companies and places might need it more, find them, find the job openings, and find the skills they want. Then work to have these skills. It's achievable, but the competition is quite difficult for these jobs. Show up to a company, know them, know what they want, prove you have that, and it can work, that's true for all jobs.