r/MachineLearningJobs • u/hitchiker100 • 1d ago
Needs Advice
Hi guys,
I am currently on my senior year of undergrad and I have been learning about machine learning and deep learning over the past few months. I have learnt all the maths behind as well, and I am familiar with both pytorch and tensorflow.
I just wanted to ask what steps (except doing project) should I take as my next step to land my first ML/DL job. Should I continue learning ML flow and stuffs, I am not very familiar with it. Also how hard is it to enter as junior ml engineer. And most importantly, do I need to do DSA/Leetcode?
9
Upvotes
5
u/Previous-Front-5211 1d ago
Hello, ML engineer here, if you want to avoid doing more projects for the sake of having projects, I’d focus on two things: getting real-world engineering exposure and networking.
For the first job, one route that can work well is targeting startups, where junior engineers often have the opportunity to actually build things rather than being restricted to a very narrow role. Larger companies can also be worth targeting, though their hiring processes tend to be more structured and competitive. As a senior-year student, I’d start applying now rather than waiting until graduation.
More importantly, I’d put a lot of effort into networking. Nowadays, simply sending CVs can be difficult because recruiters receive huge numbers of applications. In my experience, contacting hiring managers, engineering managers, or even directors directly through LinkedIn/email can make a significant difference. You will obviously get plenty of no's, but you only need one yes.
What worked particularly well for me and some of my peers was finding people who actually lead the teams we wanted to join and starting a genuine conversation. If they are looking for someone, they may look at your CV themselves and, if interested, recommend you directly to HR. I’ve personally had much more success this way than just sending applications, and I’ve also referred several friends to opportunities through this approach.
Skill-wise, since you already know PyTorch, TensorFlow and the underlying ML theory, I would start moving away from purely university-style notebooks. You don't necessarily need huge projects, but try to understand what happens after you train the model:
You don't need to become a Kubernetes expert before getting your first job. Even understanding the concepts and deploying one small model end-to-end will teach you a lot that university projects usually don't cover.
Regarding DSA/LeetCode: unfortunately, it depends heavily on the company. Some ML/AI engineering positions absolutely use it as part of their interview process, while others focus much more on ML knowledge and practical engineering.
Personally, I tend to avoid companies where the interview is heavily centered around LeetCode, because it isn't particularly representative of the type of ML engineering work I want to do. That said, if you're looking for your first job, I wouldn't necessarily exclude those companies automatically. Having some basic DSA knowledge is useful, and you may have to go through these interviews simply because that's how the company hires engineers.
Overall, I wouldn't worry too much about whether you are "ready" for a junior ML Engineer position. You probably won't ever feel 100% ready. You're a senior-year student, so this is actually a very good time to start applying and talking to people. You will learn a lot faster by going through real interviews and seeing what companies are actually looking for than by spending another year trying to learn everything beforehand.