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You know it, we know it: tech is defining our current reality, and it will shape our future. Want to guess what’s playing a major part in that transformation? You got it: machine learning. With the rise of data as the new gold (and the new prevalence of AI), machine learning is exploding in importance. The stats back this up:
- The demand for people skilled in machine learning is expected to grow by 40%, resulting in approximately 1 million new jobs.
- $158,000 is the median total pay for machine learning engineers according to Glassdoor as of the time of this writing.
- In an analysis of 1,000 machine learning vacancies, 300 were for entry-level positions.
Companies need more of these professionals, they’re willing to pay to get these people in their halls, and machine learning is open to career starters. Becoming a machine learning engineer, then, is a great way to land that reliable, well-paid career you’ve been striving for. So let’s dive into it. Here’s how to become a machine learning engineer.
What is a machine learning engineer?
What does a machine learning engineer do?
Machine learning engineer responsibilities
Within each machine learning career, responsibilities will vary, as each company and position will be slightly different, but there are overarching tendencies:
- Engineering features: They take raw data (which may have been prepared by a data engineer) and plan how to use it to achieve their business goals, keeping an eye toward reducing any bias that might be implicit in the data.
- Developing models: This is the bulk of what an ML engineer will spend their time doing. They build, train, and refine ML and other deep learning models using languages such as Python as well as frameworks such as PyTorch.
- Deploying models: They put their models into actual environments where they’ll carry out the predictions they were crafted to make. Containerization tools like Kubernetes and Docker help out here.
- Model evaluation: The model’s out in the wild, so they need to make sure it’s performing as intended using statistical evaluation and validation techniques as well as tools like MLflow.
- Collaboration: Tech work defines the field, but people in ML are going to be in regular contact with data engineers, data scientists, and other stakeholders to make sure their work is both performing well and delivering the results needed.
Machine learning engineers vs data scientists
To start off, we’ll admit that machine learning engineer roles and data science roles do often overlap, and at some organizations, the titles can be applied interchangeably. Still, there are a few main differences:
Note: Yes, Python is listed for both. It’s basically the language for data professionals, so we’d be remiss to not mention it.
Why consider a career as a machine learning engineer
You should consider a career in machine learning because it can bring you the satisfaction you’re looking for in a job regardless of what you’re looking for. Let’s start out with the basics: job security and pay. As we mentioned in the intro, predictions peg growth for these jobs at 40%, so in this career, your skills will be in high demand. And in terms of pay, the stats look really quite good:
Machine learning engineer salaries by experience
All data sourced from Glassdoor, and current as of the time of this writing.
Note: That’s just the data provided for people with the job title of “machine learning engineer.” The median total pay for a lead machine learning engineer is $201,000. More experience and more responsibility in this field can command a truly impressive salary.
Machine learning engineer salaries by location
Experience and location both influence salaries, so here’s what you can expect in different cities (and even when working remote):
All data sourced from Glassdoor, and current as of the time of this writing.
Impact
So, yes, you’ll have a reliable career that pays well. But your urge to do good in the world can also find expression in a career in machine learning engineering.
Consider Google’s LYNA. It's a tool powered by machine learning that can identify metastatic breast cancer cells in lymph nodes with 99% accuracy, even when the metastases are small. That’s as opposed to the 38% accuracy pathologists demonstrate when reviewing similar samples under similar time constraints.
Or consider Destination Earth, the EU’s project to use machine learning and data science to develop a digital twin of the Earth so that proposed government policies can be tested for potential environmental impacts.
By knowing this tech, you can play a part in developing systems that help improve health outcomes and enable policymakers to craft legislation that is environmentally conscious by design. In either case, you’ll know you’re making a difference, and those are just two examples.
The essential skills you need to become a machine learning engineer
So you want that security, that salary, that impact. Well, first, you’ll need to have the skills, which are the most crucial machine learning engineer qualifications. Which ones exactly? Dive in:
Programming
Python
This is the key language machine learning engineers use, along with its libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn. It and the tech surrounding it are the gold standard tools used in this field, so you’ll need to be adroit with them.
Additional languages
You can absolutely thrive as a machine learning engineer, especially early in your career, with only Python knowledge. But as you advance, knowing Java, Scala, and C++ can be useful, as each offers advantages for more specific use cases.
Math and statistics
Linear algebra
Mastering topics such as vectors, matrices, and matrix operations will give you the understanding you need to grasp how data progresses through models and how transformation actually works.
Calculus
Knowing how to apply derivatives and gradients will help you understand the basics underlying how models learn both through optimization and backpropagation.
Statistics and probability
To properly evaluate model performance, uncertainty, and distributions, know-how with approaches including Bayesian methods will help.
Note: This may seem intimidating, but hold on. We’re not going to pretend these skills will come to you in a second, but they are absolutely acquirable. You don’t need a PhD in math to be a machine learning engineer. You just need foundational knowledge.
Core machine learning concepts
Supervised vs. unsupervised learning
Different problems will ask for different approaches, and knowing when and why to apply supervised or unsupervised learning (e.g., spam detection vs. customer segmentation) will be vital.
Algorithms
Regression, decision trees, neural networks, SVMs — having a multiplicity of common algorithms in your wheelhouse will let you apply the right one in the right situation.
Deep learning
Modern AI runs on deep learning: CNNs, RNNs and transformers power computer vision, speech recognition, and even language models, so skills here are key.
Model evaluation
So you built the model. Does it work? Knowing how to demonstrate accuracy, precision, and recall and come to an F1 score will help you express efficacy to stakeholders.
Software engineering
Version control
Git/GitHub will enable you to keep an eye on what code went out, what changes were made, and more. When you’re experimenting and tweaking models among a team, this gives you and your colleagues the needed oversight (and a backup when something goes wrong).
System design
Your models aren’t going to be floating independently. They’re going to integrate with databases, interfaces, and other services, so an understanding of how those systems work will be crucial.
Testing and debugging
Sure, there are QA engineers out there, but you’re also going to need to have the know-how to check for edge cases, bad data, or model drift so your work comes off the line as functional as possible.
MLOps and deployment
Containerization
Using Docker and Kubernetes will help you ensure your model runs well across different environments (from your laptop to full production servers) and that it won’t go bust as traffic increases.
Cloud platforms
Fun fact: nearly all production machine learning models run in the cloud, so you should know how to interact with them via their providers: AWS, Google Cloud, or Azure.
CI/CD machine learning pipelines
Knowing how to apply continuous integration and continuous development pipelines for machine learning can help you automate testing, validation, and deployment for models, saving you time, especially when you’re making regular tweaks.
Soft skills
Communication
Yes, this is a more tech-heavy role, but you’re still going to need to collaborate with your data team — from data engineers to data scientists — so that your models work and deliver on business goals. Then, you’re going to be meeting with people outside of your data team, and they might not have the same tech acumen as you, so having the skills to explain your tech work to them will also be vital.
Problem solving and creative thinking
Spoiler: at one point, your model is going to be completely off-base, and you’ll have no idea why. You’re going to need the skills to dig into it to see just what went wrong. And another spoiler: someone’s going to submit a request that’ll boggle your mind, but you’ll still have to figure out how to build a model for them, so a penchant for creativity will help out here.
3 paths to becoming a machine learning engineer in 2025
Path 1: Self-study
If you’re learning on your own, you might be starting from zero, so to give you some guidance, here’s a rough 12-month guide for what you should study and when:
Months 1-2: Foundations
- Python: Become fluent in this language’s basics if you’re coming to the field fresh.
- Foundational math concepts: Explore topics such as linear algebra, calculus, and statistics and probability.
Resources that will help: Kaggle, Gilbert Strang’s MIT linear algebra course on YouTube
Months 3-6: Machine learning fundamentals
- Algorithms: Learn how and when to apply different algorithms and dig into the math underlying them: gradient descent, backpropagation.
- Libraries and frameworks: Now that you know how the algorithms function, master the libraries and frameworks that will speed up your work: scikit-learn, PyTorch, or TensorFlow, for example.
Resources that will help: Andrew Ng’s Machine Learning Specialization, Introduction to Machine Learning for Coders
Months 6-9: Software engineering skills
- Git/GitHub: Get a handle on version control (and build out a public-facing repository of your work).
- Design basics: Become familiar with software design patterns and system design basics.
Resources that will help: LeetCode, HackerRank
Months 9-12: MLOps and deployment
- Containerization: Become fluent in both Docker and Kubernetes and the scenarios in which each excels.
- Cloud platforms: AWS or Google Cloud are the best starting places, but Microsoft Azure is also widely used.
- Model best practices: Become knowledgeable about model deployment, monitoring, and maintenance.
Resources that will help: Made With ML, Andrew Ng’s Machine Learning in Production
Months 12+: Portfolio work
There are no core subjects to master here, so the previous format isn’t particularly relevant. The main thing here is to finally put all your new knowledge into practice. As you’ve gotten active on GitHub, you might have found projects that seem intriguing. Follow that curiosity and commit to one of those projects. It will be a valuable entry in your portfolio proving your machine learning bona-fides.
And focus on quality over quantity. You’re going to want to show off your chops to recruiters, so spend more time building fewer polished projects as opposed to numerous mediocre ones. What sort of projects? Well, if you haven’t already found something on GitHub, you can consider making a quirky image classification tool (that says how much a chihuahua’s face resembles a blueberry muffin, say) or sentiment analysis (maybe using tweets about Taylor Swift’s latest album).
Path 2: A traditional degree
If you already have a bachelor’s, you might be considering enrolling in a university to go for a further machine learning engineer degree. And some institutions do indeed offer master’s in AI and machine learning. If you’re looking to really commit to a career in research or academia in general, this is absolutely your best bet — you’ll need the credential. However, if you’re more focused on getting a job outside of academia, it’s a tough sell. A master’s is going to cost you nearly $74,000 over two years.
If you don’t have a bachelor’s and are considering this path, there’s more to talk about still. Gaining a bachelor’s will give you a good theoretical foundation, and you will likely have some networking opportunities during your studies, but finding machine learning engineer education specifically during an undergrad program will be difficult; you’ll most likely go for a computer science degree. And getting that will take four years, during which you might end up spending up to a quarter of a million dollars to finance the education.
In neither case will you get employment-focused education. It’s a degree, after all, and these institutions of higher learning, from their inception, have orbited a philosophy that prioritizes learning for learning’s sake as opposed to learning to land a job.
Path 3: A bootcamp
Pair the expert guidance of a degree program with the flexibility of self-study, and you end up with a bootcamp. Just like when you study at an educational institution, you’ll have industry-seasoned techies instructing you in the things you need to know and making sure you truly master the concepts. But instead of this knowledge being focused on theoretical underpinnings, as you’d find at a university, with a bootcamp, you’ll gain the skills that make you attractive to employers.
This won’t demand you quit your job to master the material, either; bootcamps such as TripleTen’s AI & Machine Learning program can be approached part-time, and ask you to spend only around 20 hours a week studying. That means you’ll have expectations that’ll hold you accountable, but you’ll have agency in choosing when and how to approach the learning.
Education at a bootcamp is employment-focused by design, so you’ll graduate with projects in your portfolio that you can show off to recruiters, and when you finish your studies, you won’t be hung out to dry; you’ll be supported by a wide network of career experts who’ll help you make your resume shine and work with you to polish your interview skills, for example. And if you don’t end up landing a job in your field within 10 months of your graduation, you’ll be able to request a refund, as we have a money-back guarantee.
Self study vs. degree vs. bootcamp: comparison
FAQ
Do I need a degree to become a machine learning engineer?
You don’t necessarily need a degree to become a machine learning engineer. To be sure, a degree is helpful in the employment process, but the real qualification most recruiters are looking for are practical skills. You prove that you have the requisite know-how by building out a professional portfolio that lists projects you’ve carried out.
How long does it take to become a machine learning engineer?
It can take from nine months to six years to learn the skills needed to become a machine learning engineer. This depends on your background and the learning path you choose; bootcamps will be intensive and shorter. A bachelor’s and master’s will take longer to achieve and focus more on theory. If you learn on your own, it may take even longer than that.
Is machine learning hard to learn?
Machine learning isn’t the easiest field to master, but learning the skills for the profession is eminently possible. You’ll have to gain know-how with programming, math, stats, and data practices, which might seem daunting, but doesn’t have to be. With just 20 hours of training per week over nine months, you’ll master all you need to know with TripleTen’s AI & Machine Learning program.
What programming language should I learn for machine learning?
You should first learn Python for machine learning. It is the most widely used language in the field since it offers simple syntax and readability, extensive libraries focused on machine learning, and has a robust community you can find support from online. After that, you might consider learning Java, Scala, or C++.
Can I learn machine learning on my own without formal education?
Yes, you can absolutely learn machine learning on your own without formal education. Many successful people in the field are self-taught or found employment by following alternative educational routes such as tech bootcamps. The main thing is about getting the know-how employers are looking for and building out a portfolio to prove your bona fides.
Are machine learning certifications worth it?
Machine learning certifications are worth it later in a machine learning career, but they are not vital for people seeking junior or entry-level roles. Once you do start a machine learning career, certifications can help you achieve promotions and keep you attractive to employers, as they prove you’ve pursued continuing education throughout your career.
What math do I need to know for machine learning?
For machine learning, you need to know linear algebra, calculus, statistics, and probability. Linear algebra will help you grasp how data moves through models, calculus will help you understand the process by which models learn, and statistics and probability will help you evaluate model performance. Crucially, you don’t need to be an expert in math, though; foundational knowledge of these subjects will be enough.
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