wish to be machine learning engineers.
I get it.
It’s an important job, with interesting work, great pay, and overall, it’s very cool.
Nevertheless, it’s definitely not a walk within the park to turn out to be one. In this text, I aim to supply my unfiltered and candid advice to aspiring machine learning engineers.
This will probably be more of a pep talk, providing you with clear expectations of what it takes to turn out to be a machine learning engineer and whether it’s something you really need to pursue.
Learn every week
If you ought to turn out to be a machine learning engineer, then you could dedicate at the very least 10 hours each week to studying outside of your on a regular basis responsibilities.
I’m sorry if that upsets you, but again, if you ought to land a job within the highest-paying tech career, you could put in additional effort and time than other people. There may be simply no way around it.
Without sounding smug, I learn something recent in machine learning each week, although I work full-time, create YouTube videos, exercise five times per week, and have mentoring and training clients. If I could make time, so are you able to. All of it comes right down to priorities.
Almost all the things I’ve achieved in my profession comes from consistently studying and documenting my learning outside of labor. I’ve written over 150 technical articles on Medium on topics akin to:
…and plenty of more. You possibly can see the whole list here.
This isn’t to boast but to point out the extent of commitment required to turn out to be a machine learning engineer.
Consider this career in the identical category as lawyers, doctors, or accountants. These fields demand years of study and practice. The identical is true for machine learning; it’s rarely seen as that because of its relative newness.
I often say:
It’s easy to know what you could do but hard to do it consistently over time. There isn’t a secret; you might have to take the long road.
So, pick something you ought to learn and follow it until the top; then, recycle this process time and again. That’s all there’s to it.
Extend your time horizon
Even with essentially the most ideal background, it would still likely take at the very least two years to turn out to be a completely qualified machine learning engineer at a top company.
Don’t fall into the trap of pondering that just a few online courses and projects are enough to land a job in certainly one of today’s highest-paying tech roles.
Online certifications make it easier to learn the content in data science and machine learning, which may be very helpful. Nevertheless, they rarely help you get hired nowadays, especially in our tough job market.
I don’t say this to discourage you but to set realistic expectations. I’ve spoken with many individuals who attempt to shortcut their journey, and I’ve yet to see it succeed.
To turn out to be a machine learning engineer, you would like solid foundations in:
- Mathematics
- Statistics
- Machine Learning
- Software Engineering
- DevOps
- Cloud Systems
A few of these skills can only be developed through real-world experience. That’s why I normally recommend people start as data scientists or software engineers first after which pivot to machine learning engineers, because it’s not an entry-level role.
Accepting the undeniable fact that it would take you just a few years to turn out to be a machine learning engineer is liberating and takes the pressure off you.
Take your time to learn things deeply, really study, and your knowledge will construct over time. I promise, eventually, you’ll be ready for that ML engineering role when the time is correct.
Stop chasing AI
Newsflash: A machine learning engineer is an AI engineer. So stop pondering that calling a chatbot API like ChatGPT or Claude makes you a machine learning engineer.
As a machine learning engineer, you’re expected to deeply understand how models/algorithms work and have a firm grasp of statistical learning theory and all the basic mathematics.
Meaning knowing core algorithms like:
.
Most individuals claim know them, but you’ll be surprised at how little you truly know.
I’ve mock-interviewed countless candidates, and plenty of can’t even explain gradient descent from first principles using calculus.
Again, I’m not attempting to be harsh but to point out you the fact I actually have seen.
I all the time tell people to stop rushing to learn flashy topics like NLP, computer vision, or generative AI.
Your first few years ought to be about mastering the basics; mastering them thoroughly so you might have a solid understanding for a lot of machine learning theory interview.
The fact is that the majority machine learning engineer roles primarily deal with classical supervised learning. Your job is usually less about constructing exotic models and more about tailoring well-understood algorithms to resolve specific problems. That’s why a deep understanding of the fundamentals is important.
If you ought to test your fundamental knowledge, I offer mock interviews based on real questions I’ve faced in actual ML job interviews. Be at liberty to ascertain it below.
Mock Interview with Egor Howell
topmate.io
It is vitally hard
Let’s end with something that may appear a bit obvious: becoming a machine learning engineer is just hard.
As I’ve said throughout this post, the role demands expertise across a big selection of disciplines. You’ll need strong foundations in maths, statistics, and programming, plus real-world experience as a software engineer or data scientist first (that are tough jobs in their very own right). Moreover, you need to commit to continuous learning throughout this complete period.
Even with essentially the most perfect background — a STEM master’s or PhD — it’s still an extended, difficult journey. When you’re coming from a non-traditional path, it’s even harder. That doesn’t mean it’s inconceivable, however it is harder, and you could resolve if the challenge is value it for you.
I often say:
It takes sustained effort for at the very least just a few years.
You’ve got to be honest with yourself about whether you’re willing to speculate 2–3 years minimum (and, generally, 4–5 years) to interrupt into the sector.
For me personally, giving up 4 years for a decades-long profession doing work I really like is totally value it. But that’s a calculation only you possibly can make.
Actually, I find it liberating that it’s so hard, because it makes me feel higher about struggling through it.
I’m someone who doesn’t sugarcoat anything, and you may have noticed that the majority of my points boil right down to two key aspects: effort and time.
Anything value doing often requires consistent effort over an extended period. That’s the secret to becoming a machine learning engineer.
When you are serious about becoming a machine learning engineer, then I like to recommend trying out the below article, where I detail my roadmap:
Link.
One other thing!
I offer 1:1 coaching calls where we will chat about whatever you would like — whether it’s projects, profession advice, or simply determining the next move. I’m here to make it easier to move forward!
1:1 Mentoring Call with Egor Howell
topmate.io
