All right, so you clicked on this video because you want to know how to land a machine learning engineer role in 2024 or 2025, and I'm going to tell you how to do that because I just landed a machine learning engineer role at a top tech company.

And I did not have to do a PhD or some of the advice out there, um, that is currently on the internet. And I had to do so much research to figure out how to get on a team like this or what direction I should take just because of all the advice out there.

So I'm hoping that this can help someone, um, kind of understand what goes through the mind of a hiring manager that is trying to hire for these types of roles and more importantly, who I've seen get hired in these roles and how I basically went about it.

So I think to first understand what you need to land an ML engineer role, you first need to understand what an ML engineer is. What does that even mean in this industry? An ML engineer or a machine learning engineer is someone that has a software engineering background or skill set, but also has some sort of specialty or experience in ML products or ML research, right?

So there's a lot of domains an ML engineer or this software engineer that's working on an ML team would have options for, such as they could be working on a computer vision project. You could be an ML engineer on a gen AI project. You could be working on more traditional ML on the statistics side and just on recommendations in general. Um, there's so many types of ML engineers, and it really comes down to when you're applying for

These roles finding out hey which section of ML am I applying for or which type of team at a company would I like to work for right And that will kind of give you a better idea on how to actually land these roles That's why at a lot of these companies you'll see the

listing as software engineer comma ML AI or like ML at Meta that's very common uh machine learning engineer or software engineer machine learning same thing okay research engineer is also basically the same thing as a machine learning engineer except you're more on the research side and probably working with

research scientists who usually have PhDs and are actually implementing papers doing really heavy math and kind of experimenting and coming up with those ideas because they've been doing a PhD in that section of ML for a while so research engineers or ML engineers might work with research scientists who have

those PhDs um uh depending on what that team at that company is working on Now that you know what an ML engineer is how it's a newer term and how there's a large range of topics how do you actually land an ML engineer role and

the main way you land an ML engineer role that I basically discovered or have consolidated with all my expert writing and also landing the role and seeing friends land the roles as well is you need one of two types of experiences or projects on your resume to get that

interview okay you need either an industry project meaning hey you are already working a software job and you get an ML project right that could be your into that section If I got a gen AI project at a job I'm already working at as a software

engineer or data engineer that's going to let me get the skill set relevant to gen AI as an ML engineer and I can start after a couple years or a year applying to gen AI ML engineer roles right but here's the thing what if you're a master's

student or you're just doing a general software engineering job you don't know what to study or do and that's kind of the position I was in right I could just do some side scikit-learn projects do those random tutorials that everyone is giving information about on the internet but

random tutorials are not going to get you the job random ML courses even intro courses right there's a very famous one by Andrew Ng and that course is very good to get an intro to ML to learn maybe what it's about but if you actually want to land an ML engineering role at a

company doing like some some sort of convolutional neural network on a breast cancer data set and being like oh here's my accuracy and then yeah maybe like I tied it in with this model and putting on on your resume with PyTorch that's

not going to cut it okay what you really need and that I've really noticed is relevant and I've even talked to I guess engineering managers in the ML space at different FAANG companies as well but what you need is one of two things I already said the industry

project the second thing if you're in that pickle that I described is you can do an academic project at a university or try to do some sort of academic project on your own that's the last option but really doing an academic project in general and what do I mean by

that if you want, let's say a job in ML compilers, or I won't say ML compilers, but let's say computer vision. If you download or look at the latest Vision Transformers papers at these conferences, um, such as CVPR and ECCV, or

just reach out to your local university and ask them, 'Hey, these are some cool papers you guys are working on in the computer vision space. Can I contribute? Can I be a research assistant? Can I help?' Or, 'Hey, I'm doing this project. Can I get some credits for it as part of my

Masters or anything related to that? The more academic, the more I guess of an academic approach you take with it, that's one way to do it. Now, you might be like, 'Wait, you just said you don't have to do a PhD or an M. You don't. You can do

this through a Masters. If you do a Masters, such as at Georgia Tech online, there's a project option, not a thesis, a project option where you can just do any project, choose a specific ML project you want to do, get a professor to sign off on it, and all of a sudden

now you have that ML project that's going to be on your resume and relevant to that hiring manager for that specific ML engineer team you're applying to. And I think what I'm trying to get at is you really want to think about this more from the

perspective of a hiring manager. If you're a hiring manager and you are hiring for an ML compiler engineer, right, a very specific type of ML engineer, you know what they might consider valuable is someone that's actually implementing or working with this stuff. Any hiring

A manager wants to hire someone that's doing something very similar to what their team is already doing. That's why an industry project's really good, or an academic project directly relating to it, is really good for the project their team is working on. And so imagine if you

wanted this ML compiler engineer role, so you go and contribute to online ML open-source compilers such as Triton, or you make projects utilizing these formats. I know there's one I think it's called MLIR that's another famous one that a lot of these ML compiler engineer roles

list, and you can just go and start playing with these open-source tools and do a personal project. Maybe look up a paper in that space and try to basically learn about it. And if you can have that project on your resume, that's demonstrating you're learning something

that's directly related to the team, that can get you a very strong foot in the door because there's very few engineers who have very specific types of expertise in these ML topics, right? If I was a hiring manager at Meta, um, hiring for an XR or an AR type role, which by

the way, not an engineering manager, so you can take it with a grain of salt, but my strategy for trying to make them interview me would be, hey, let me do a VR project that's um, related to like computer vision. Do something with vision transformers, maybe take one of the

latest papers that got pushed out and wait, let me just tinker with this model, make a modification. I don't have to go and publish a paper. If you publish a paper, whether working with a local university or not, that's cool too. That's going to be even more important, but you

just need something that's directly mapping from your resume to the project and that's really all you need is either you can do it through the industry project or you can do it through the academic project now my last thing is going to be that if you don't know what

type of ML engineer role you want you're kind of just out there to explore and you enjoy the whole field in general that's great too but I would still encourage you to be putting projects whether through your job you can do this in the form of being

like hey I don't have an ML project right now but is there a hackathon can I do a proof of concept for like this Vision Transformer or this ML concept I find interesting can I just showcase it at a hackathon for 2 days in the company

or is there some MLOps Club at my company that just talks about papers and we make some random proof of concept you can put that stuff on your resume because you're technically doing those projects or that exploration at that company and that can be another way to kind of get industry

experience even if you're not working directly on an industry project you can work with people in your company that are talk to them schedule coffee chats etc. and so when it comes to the academic side right if you want to do a masters and then as part of your Masters do a

thesis or a project on a specific type because you know what specific type of ML you're interested in then you can do that and you could probably drink a mango lassi for every single time I said ML in this video and get diabetes and I know this video

also isn't like the most easy to understand, but I'm going to be putting out more content on how to land an ML engineer role. And honestly, actually, I kind of lied; I might not put out more content on how to land an ML engineer role, but I will be

putting out more content in general. And I just felt like posting a video today, so yeah, I hope this helped someone. And if you have any questions at all about how to land like an ML engineer role at a top tech company, or hey, this is what

I'm doing, what should I be doing for X, Y, and Z, then please leave a comment, and I'll actually respond, even if only like in a span of two years, 10 people see this video, but they're very niche, and they want that response. I I'll like I'll

just straight up respond. Um, and then also one more thing, by the way, is you can lock into these roles if you're a generalist. It's it's kind of stupid to think there isn't luck involved. Now, what luck means to you

that's debatable, but when I say lucky, is I don't mean completely out of your control. What I mean is that some companies at certain times will be opening up more ML roles and will have more positions from more teams opening. And because of that, when you have these

projects, when you have your preparation, when your preparation meets opportunity, you can be lucky. And I think doing the most you possibly can up until that point is very important. And sometimes you might be really lucky where you only did one project, or you just started your Masters

and maybe that just got you through the door and you were interview ready and you remember you need to still have that software engineering skill set whether that means elite coding or system design or actually having experience that's depends on the role but yeah and also here's some really good ML engineering

channels I see right here A B and C I'll link them in the comments below because they have really good advice that I actually thought was practical useful and yeah some of them even don't have a lot of subscribers but the advice they're giving is gold and their background is

gold um so yeah I hope this helped do projects directly related to the roles you're interested in even if it's just to learn it might help you roll across ML roles whatever blah blah blah and yeah I'll see you guys in the next video see

How to Get Machine Learning Interviews in 2025

Haris Hussain

이 동영상은 머신 러닝 엔지니어 역할을 확보하는 방법을 설명하는 것을 목표로 합니다.

연사는 박사 학위가 필요하지 않다고 강조합니다.

연사가 채용 관리자의 관점에서 바라본 인사이트를 공유합니다.

ML 엔지니어는 소프트웨어 엔지니어링 기술과 ML 전문 지식을 보유하고 있습니다.

ML 엔지니어는 다양한 영역(컴퓨터 비전, 제너레이티브 AI, 기존 ML)에서 일합니다.

직책에는 종종 '소프트웨어 엔지니어, ML/AI'와 같은 변형된 직책이 포함됩니다.

연구 엔지니어는 비슷하지만 박사급 연구자와의 연구 및 협업에 더 중점을 둡니다.

ML 엔지니어 역할을 맡기 위한 필수 경험

이력서에는 산업 프로젝트 또는 학술 프로젝트가 중요합니다.

산업 프로젝트에는 기존 소프트웨어 엔지니어링 역할 내에서 ML 작업을 수행하는 것이 포함됩니다.

일반적인 온라인 튜토리얼과 강좌만으로는 역할을 확보하는 데 충분하지 않습니다.

학술 프로젝트에는 대학 연구 또는 독립 프로젝트에 기여하는 것이 포함될 수 있습니다.

석사 프로젝트는 학술 프로젝트 요건을 충족할 수 있습니다.

채용 관리자는 팀의 업무와 직접적으로 관련된 경험이 있는 지원자를 우선적으로 고려합니다.

관련 분야의 오픈소스 프로젝트에 기여하는 것은 매우 유익합니다.

이 발표자는 특정 역할에 대한 전략적 프로젝트 접근 방식의 예를 제공합니다.

일반인의 경우 해커톤이나 회사 내부 프로젝트에 참여하여 관련 경험을 쌓을 수 있습니다.

해커톤에서 선보인 개념 증명 프로젝트는 이력서에 포함할 수 있습니다.

특정 ML 분야에 대한 석사 학위 논문도 강력한 자산이 될 수 있습니다.

발표자는 운의 역할(기회 회의 준비)을 인정합니다.

연사가 유용한 ML 엔지니어링 채널을 추천합니다.

How to Get Machine Learning Interviews in 2025

How to Get Machine Learning Interviews in 2025

스크립트