Unlike software engineering or software development the entry barrier for a machine learning engineer is still very high and it hasn’t changed much over the last 5 years Being an ML engineer at FAANG still requires one to have a masters or PhD. So how can you enter this field? I interview for Amazon’s applied scientist post and cleared 3 rounds. Even though I was just a bachelors degree holder I got shortlisted and performed well for the 3 rounds. So how did I do it? Why was I shortlisted? Is it because I’m from tier 1? Not necessarily, cause I did have a talk with Amazon employees as to what it takes to be an applied ML scientist at Amazon. Having a masters or phd surely increases your chances but here’s how you can get shortlisted and probably selected as well for an applied ML scientist position at Amazon or other FAANG or bit MNCs. The first important thing to have is, prior work experience to prove that you’re proficient with ML. It can either come through internships, projects or publications. Fortunately I had them all from my undergraduate studies, so it increased my chances. Having a good kaggle rating is really really important if you don’t have internships or projects to write. Very few people do kaggle regularly and it’s really a game changer. Even I didn’t do kaggle much in college. Practice maths behind ML is I cannot emphasize how important maths is. Most of the students I talked to, when I ask them why they used a certain model or certain type of architecture or regularizer for this problem stumble to give me any strong answer cause their solution was not mathematically motivated but rather something they found on tutorials or internet. It’s fine to use tutorials but you should know why you’re doing this and not something else. Maths hone your intuitive thinking as well. Given a new problem how to approach it from ground up Last but not the least Be good at Python. Don’t write crappy unreadable and non reproducible code ML is all about reproducibility of experiments.
The biggest problem with these roles is the Demand-Supply. Nowadays, even IIT/IISc are churning out MTech/PhD in huge numbers. Even if one follows the best practices and becomes proficient, it's still not enough because, with all likelihood, there will be at least few, even if slightly better than you. The no. of open positions is limited, unlike SDE, and the companies tend to pick the best 1 or 2 available to them, not 10 good ones, and that's only after interviewing. But to begin with, these 1-2 posts have 100s of applicants, and I have often seen the best people known to me not even get an interview call.
Do we really need to spend time honing our python skills, considering we have so many coding assistants available now?
Owh, must be Master or PhD?
This is so good, thankyou Saurabh Kumar loved this💪🏻
Harshit Singh check this once
Useful tips
Very informative!
Founder @ Baton | previously @ Meta, Instagram, StubHub | Summa cum laude from UPenn
3moTo clarify, some FAANG companies do not have the concept of "ML Engineer". At Facebook for example, there are "Software Engineer, Machine Learning" roles just like there are "Software Engineer, Compilers". In these jobs, the idea is that you are a software engineer first and then someone who specializes in ML second. A lot of roles that I worked in Search, Ads used ML but basic SWE practices were more important. Also in most cases there is no restriction on your level of education - some of my bachelor's classmates ended up doing amazing research on even FAIR.