Introduction to Machine Learning Engineering
Many people want to be machine learning engineers. It’s a great job, with interesting work, great pay, and overall, it’s very cool. However, it’s definitely not a walk in the park to become one. In this article, we will offer candid advice to aspiring machine learning engineers, providing clear expectations of what it takes to become a machine learning engineer and whether it’s something you really want to pursue.
The Importance of Continuous Learning
If you want to become a machine learning engineer, then you need to dedicate at least 10 hours each week to studying outside of your everyday responsibilities. This may seem like a lot, but it’s necessary to land a job in the highest-paying tech profession. Without sounding arrogant, many machine learning engineers learn something new every single week, even though they work full-time and have other responsibilities. It all comes down to priorities.
Almost everything achieved in a career comes from consistently studying and documenting learning outside of work. This includes writing technical articles on topics such as machine learning, deep learning, and natural language processing. This isn’t to boast but to show the level of commitment required to become a machine learning engineer. Think of this profession in the same category as lawyers, doctors, or accountants, which demand years of study and practice.
The Key to Success
The key to success is to pick something you want to learn and stick to it until the end, then recycle this process again and again. That’s all there is to it. It’s easy to understand what you need to do, but hard to do it consistently over time. There is no secret; you have to take the long road.
Extending Your Time Horizon
Even with the most ideal background, it will still likely take at least two years to become a fully qualified machine learning engineer at a top company. Don’t fall into the trap of thinking that a few online courses and projects are enough to land a job in one of today’s highest-paying tech roles. Online certifications help you learn the content in data science and machine learning, but they rarely allow you to get hired, especially in a tough job market.
To become a machine learning engineer, you need solid foundations in mathematics, statistics, machine learning, software engineering, DevOps, and cloud systems. Some of these skills can only be developed through real-world experience. That’s why it’s usually recommended to start as a data scientist or software engineer first and then pivot to machine learning engineer, as it’s not an entry-level role.
Setting Realistic Expectations
Accepting the fact that it will take you a few years to become a machine learning engineer is liberating and takes the pressure off. Take your time to learn things deeply, really study, and your knowledge will build over time. Eventually, you’ll be ready for that ML engineering role when the time is right.
Stop Chasing AI
A machine learning engineer is not an AI engineer. So stop thinking that calling a chatbot API 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 fundamental mathematics.
Mastering the Fundamentals
Most people claim to know the core algorithms, but you’ll be surprised at how little you actually know. I’ve mock-interviewed countless candidates, and many can’t even explain gradient descent from first principles using calculus. Stop rushing to learn flashy topics like NLP, computer vision, or generative AI. Your first few years should be about mastering the fundamentals, so you have a solid understanding for many machine learning theory interviews.
The Reality of the Job
Let’s end with something that might seem a bit obvious: becoming a machine learning engineer is just hard. As said throughout this article, the role demands expertise across a wide range of disciplines. You’ll need strong foundations in math, statistics, and programming, plus real-world experience as a software engineer or data scientist first. Additionally, you must commit to continuous learning throughout this entire period.
The Calculation
Anyone can become a machine learning engineer, but that doesn’t mean everyone should, or even wants to. It takes sustained effort for at least a few years. You have to be honest with yourself about whether you’re willing to invest 2–3 years minimum (and, in most cases, 4–5 years) to break into the field. That’s a long time. For many, giving up four years for a decades-long career doing work they love is absolutely worth it. But that’s a calculation only you can make.
Conclusion
In conclusion, becoming a machine learning engineer requires time, effort, and dedication. It’s not an easy journey, but it can be rewarding for those who are willing to put in the work. If you are serious about becoming a machine learning engineer, then it’s recommended to check out resources that detail a roadmap for success. Remember, anything worth doing often requires consistent effort over a long period. That is the secret to becoming a machine learning engineer.
