How to Become a Machine Learning Engineer & Get Hired in 2026

Machine Learning is one of the hottest fields in tech right now, but how do you get into it? Well, you read this guide, of course!

• Do you need a degree to get started or get hired? Nope

• Are there job opportunities? Yep… thousands in the US alone

• How much does it pay? A lot!… $100,000+ is the average of those available jobs

In this guide, I’ll pull back the curtains for you and show you exactly what it takes to become a Machine Learning Engineer.

I’ll also cover exactly what a Machine Learning Engineer does, the skills required in the role, and how to get that all-important experience you need to land a job.

How to become a Machine Learning Engineer without a degree!

So just a quick heads up. The steps I’m about to give you are based on our Machine Learning Engineer roadmap:

I’ll go into more detail around everything you need to know in this guide, but if you want a quick follow-along cheat sheet in the future of what to learn and in what order, then feel free to follow it.

And with that out of the way, let’s break this all down.

Optional step. Speed up your learning

Because you’re going to be learning a lot of new skills to become a Machine Learning Engineer, I recommend taking a slight detour and checking out this guide and this course:

Average time to complete: 5 days

This course will teach you how to learn using concepts you’ve never heard of before.

Why care?

Because it’ll help you learn faster, which will then reduce the total time it takes you to learn all these other skills you’ll need. (It’s kind of like stopping the car to fix a flat tire, because you know it will make the whole journey much quicker and smoother).

Like I say, it’s optional but definitely worth it.

Step #1. Learn the core required skills

Once you’ve done that, it’s time to learn the core skills to become an ML Engineer.

The bad news is there’s a lot to learn. However, the good news is that I cover all of it inside just one course:

Estimated time to learn: 3 months. (However, this is based on the average students’ feedback. You can do this faster, it depends on how much time you can spend learning each week).

Here’s a mile-high overview of everything you’ll learn and why:

• Python. This is the programming language that powers almost all ML work. You don’t need to master it before starting, as my course covers everything you need as you go

• Data Exploration and Visualizations. Before you can build models, you need to understand your data. Which is why you’ll learn how to explore, clean, and spot patterns that guide your decisions

• Neural Networks and Deep Learning. This is the core architecture behind modern AI systems, from image recognition to language models. This is where ML gets really powerful and starts to learn from the data we give it

• Model Evaluation and Analysis. Building a model is only half the job. You’ll also need to know how to measure whether it actually works and where it’s falling short

• NumPy. This is the foundation of numerical computing in Python, and almost every ML library is built on top of it, so understanding it makes everything else easier

• Scikit-Learn. This is the go-to Python library for building, testing, and comparing ML models. You’ll use this constantly in real-world projects

• Machine Learning Project Workflows. It’s all good knowing the techniques, but you also need to learn how to put it all together from start to finish. i.e., How to take a problem from raw data all the way to a working model. Knowing this process is what separates engineers from people who just followed a tutorial

• Data Visualization with Matplotlib and Seaborn. You’re never the only one looking at the data. That’s why you need to learn these tools so you can turn your data into charts and graphs that tell a clear story for presenting findings to non-technical stakeholders

• Transfer Learning. Instead of training models from scratch, you need to know how to reuse and adapt pre-trained models. This saves huge amounts of time and often produces better results

• Image Recognition and Classification. You’ll also need to know how to teach models to understand and categorize visual data, one of the most common real-world ML applications

• Train/Test and Cross Validation. It’s all very good using pre-existing data for training, but how do you know if it’s working when you add in new data? That’s why you need to learn how to evaluate your models

• Supervised Learning such as Classification, Regression, and Time Series. These are the most common ML problem types you’ll encounter on the job, covering everything from predicting house prices to forecasting sales, so you need to know how to use them

• Decision Trees and Random Forests. These are interpretable models that are widely used across finance, healthcare, and beyond. Great for when you need to explain your model’s decisions. Another key skill to have

• Ensemble Learning. Sometimes you’ll need to combine multiple models to get predictions that are more accurate and robust than any single model alone

• Hyperparameter Tuning. It works, but it can do better! That’s why we use this fine-tuning process to get the best possible performance out of your models once the basics are working

• Pandas. This is the essential Python library for manipulating, cleaning, and transforming data before it goes anywhere near a model

• Deep Learning with TensorFlow and Keras. You’ll learn to build serious neural networks from scratch, covering the techniques used by companies like Google, Meta, and OpenAI

• and much more!

So yeah, quite a few things to learn, but the good news is that by the end of this, you’ll already have the skills to start applying for junior ML Engineer roles.

Better still, I’ve also gone ahead and shared the first 10 hours of the course below for free:

Just be aware that the course is updated regularly each year, whereas this video is a few years old now.

So now you have the core skills required for the role, let’s look at getting hired!

Step #2. Build a portfolio to prove you can do the work

Another slight detour, but 100% required. 

So, the really cool thing about working in tech is you don’t need a degree to get hired. However, you do need to prove you can do the work, and that’s where portfolios come in. You set one up and share your work there so that prospective employers can see what you’ve done and then possibly hire you.

You can learn how in the course below:

Average time to complete: 10 days

Once you’ve built your portfolio, make sure to go ahead and add in any projects that you’ve built so far!

Step #3. Apply for ML Engineer jobs

Although there are more skills you can learn (and we’ll cover them in a second), by this point, you now know enough to start applying for Junior ML Engineer jobs.

That being said, the tech interview process is a little more in-depth and longer than most other jobs you will have applied for before. But lucky for you, I wrote an entire free guide called The No BS Way To Getting A Machine Learning Job that you can follow!

I also recommend checking out Andrei’s course on getting hired at your dream job:

Average time to complete: 12 days.

He covers the entire application and interview process in detail, including his technique, where he gets a 90% interview success rate!

That being said, it can take a few months of applications to get a job locked in, so let’s look at some further skills you can learn…

Step #4. Skill up further

Don’t get overwhelmed by the number of skills here. As I said before, you already have enough to get hired. These are just additional skills that can help you be more appealing to employers or even start in more senior roles.

So let’s break them down.

Learn Hugging Face

Hugging Face is one of the most important ecosystems for the world of modern Machine Learning and AI, simply because it makes working with state-of-the-art AI and Machine Learning models surprisingly easy for beginners -while still allowing extreme versatility for more advanced users.

This is why companies such as OpenAI, Google, and Apple all share their open-source models there, while individuals can create their own profiles and begin building their ML/AI portfolios.

So it’s definitely worth learning how to use, and the good news is, I have a course on it:

Average time to complete: 28 days

I’ll show you how to customize real machine learning models with Hugging Face as the base, including text classification models, object detection models, large (and small) language models, vision language models and more.

We also cover:

• The powerful Hugging Face Transformers library for customizing machine learning models in text, computer vision, audio, video, and even a mixture of all of them with multimodal models, for both inference and training.

• The Hugging Face Datasets library to easily access and share AI datasets for Audio, Computer Vision, and Natural Language Processing (NLP) tasks.

• The Hugging Face Hub, an online platform that allows people to easily collaborate, build, and share their machine learning projects.

Pytorch

PyTorch enables you to craft new and use existing state-of-the-art deep learning algorithms like neural networks, which power much of today’s Artificial Intelligence (AI) applications.

• Tesla uses it to build the computer vision systems for their self-driving cars

• Meta uses it to power the curation and understanding systems for their content timelines

• Apple uses it to create computationally enhanced photography.

Want to know what’s even cooler?

A lot of the latest machine learning research is done and published using PyTorch code, so knowing how it works means you’ll be at the cutting edge of this highly in-demand field.

So yeah, definitely worth learning, and wouldn’t you know it, but I have a course on this also:

Average time to complete: 50 days

You’ll learn Deep Learning with PyTorch by building a massive 3-part real-world milestone project. By the end, you’ll have the skills and portfolio to get hired as a Deep Learning Engineer.

Tensorflow

TensorFlow is the other big hitter in deep learning frameworks with companies like Google, Airbnb, Uber, DeepMind, Intel, and IBM all running on TensorFlow.

And yep, I have a course on this also:

Average time to complete: 60 days

In this course, you’ll go deep on building real neural networks from scratch across three hands-on milestone projects:

• Food Vision which is a computer vision model that identifies over 100 types of food using transfer learning

• SkimLit which is an NLP model that classifies medical research abstracts to help researchers skim literature faster

• BitPredict, which is a time series forecasting model that replicates a state-of-the-art algorithm to predict Bitcoin price movements

All handy to learn and flesh out that portfolio even more.

Learn how to deploy models with SageMaker

Knowing how to build a model is one thing. But knowing how to deploy it at scale in a production environment is what takes you from junior to mid-level.

That’s where AWS SageMaker comes in.

It’s one of the most widely used tools in the industry for training, fine-tuning, and deploying machine learning models at scale, and it’s the kind of skill that appears on more senior ML Engineer job listings.

Average time to complete: 28 days

In this course, you’ll build end-to-end projects using real datasets, Hugging Face models, and PyTorch. (All things you’ve already learned earlier in this roadmap).

You’ll then deploy those models to production with AWS, writing your own training scripts, monitoring jobs in CloudWatch, deploying scalable endpoints, and stress-testing your models with production traffic.

And good news?

You can check out the first 4 hours of this course in the video below for free:

Learn Prompt Engineering

One of the key skills in 2026 is learning how to work with AI. More specifically, not just building with it, but learning how AI works under the hood and how to prompt it effectively to get the results you want.

Not only can this make your life a lot easier, but it’s often a core skill being asked for in a lot of job applications:

Average time to complete: 24 days

You can check out the first 5 hours of this course for free below:

Again, though, just be aware that these free videos are not updated as often as the actual courses. I definitely recommend taking the course and diving deep into this.

Learn how to fine-tune LLMs (with QLoRA, AWS, and Open Source)

Most ML Engineers know how to use existing models. The ones who can take an open-source LLM and customize it on proprietary data for a specific business use case? That’s a much smaller, much more valuable group.

Now, you’ll have already learned fine-tuning techniques earlier in the main course. However, this course teaches you how to do it using QLoRA, which is a technique that dramatically cuts the compute resources required.

Why care?

Simply because this makes it far more practical to run on real business infrastructure.

Average time to complete: 28 days

You’ll learn how to preprocess proprietary datasets, run fine-tuning scripts, monitor training jobs in CloudWatch, and deploy your customized LLM using AWS SageMaker and Streamlit.

So that by the end, you’ll have a production-ready, fine-tuned model you can add straight to your portfolio.

Learn Data Structures and Algorithms

OK so this right here is the type of stuff you usually learn in a computer science course. It’s the fundamentals of how tech works, and most people miss this or only learn it when they want to interview.

However, it’s also worth knowing to round out your skills, as it’s a core component of moving into more senior roles.

Why?

Well, you can know how to do something with a tool. But unless you understand the other limitations and external factors, you might not make the best choice or understand potential solutions. This is taking your current skills and then adding to them so you’re more rounded:

Average time to complete: 40 days

Many developers who are “self-taught” feel that one of the main disadvantages they face compared to college-educated Computer Science graduates is the fact that they don’t have knowledge of algorithms, data structures, and the notorious Big-O Notation.

This course will teach you how it all works, so whether it comes up in an interview or on the job, you’ll know exactly what you’re talking about.

Statistics with Python

Finally, this last course is optional but worth it.

Why?

Well, most ML Engineers can build models. But the ones who truly understand why a model is performing the way it is are the ones who have a solid grasp of statistics under the hood.

Things like confidence intervals, hypothesis testing, and regression analysis aren’t just academic concepts. They’re the tools you use to validate your models, interpret your results, and make the kind of data-driven decisions that make you stand out in more senior roles.

Average time to complete: 42 days

You’ll learn everything from descriptive statistics and probability through to multilinear regression and survival analysis, all using Python. And yes, you’ll also learn how to use AI to work with data more efficiently and securely along the way.

Become a Machine Learning Engineer today!

So there you have it – the entire roadmap to become a Machine Learning Engineer within the next 6-12 months, or sooner. As well as the path to move up into more senior positions later on.

Not bad for a complete career change with zero prior experience and a six-figure salary, right!?

Machine Learning is a really great career to get into right now. High demand, and a whole host of new companies diving into ML and testing it for themselves and their industries.

Better still, it’s not as difficult to pick up as some people make it out to be; it just takes a little determination and hard work. You’ll be surprised how fast you can learn and start working in this role if you follow a set path as I’ve recommended.

Keep me updated on your progress!

P.S.

Want some great news?

All of the courses I’ve mentioned in this guide are included in a single Zero To Mastery Academy membership. That means if you become a member, you have access to all of these courses right away and will have everything you need in one place.

Plus, as part of your membership, you’ll get to join me and 1,000s of other people (some who are alumni mentors and others who are taking the same courses that you will be) in the ZTM Discord.

Ask questions, help others, or just network with other Machine Learning Engineers and tech professionals.

Make today the day you took a chance on YOU. There’s no reason why you couldn’t be applying for Machine Learning jobs in just a few months from now if you just follow the steps I outlined and put in the hard work.

So what are you waiting for 😀?

Come join me and get started on becoming a Machine Learning Engineer today!