How to Become a Machine Learning Engineer in 2021
Everyone knows machine learning is a hot skill and a lucrative one. According to Glassdoor, the average base pay for a machine learning engineer as we enter 2021 is more than $114,000 per year in the United States. Many employers also have more perks, such as bonuses and equity, that can amount to much more than your base pay as your machine learning engineer career progresses.
Landing a machine learning engineering job isn’t easy, however. The skills and experience you need are broader than you may think, and they fall under a variety of different job titles.
Last Updated September 2020
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What is a Machine Learning Engineer?
Let’s start by unpacking what this particular job title means. It’s actually a fairly unusual title in the field of machine learning and data science. If you look at the biggest employers such as Amazon, Google, and Apple, only Apple uses “Machine Learning Engineer” as a job title. Amazon tends to hire “Machine Learning Scientists” or “Software Engineers” that happen to specialize in machine learning. While Google posts for “Software Engineer, Machine Learning” roles.
The key term is the word “engineer” – this tells you that the job is very hands-on, and employers expect you to write code on a day-to-day basis. It’s not going to be just about building models and dealing with theory. You’re going to actually build machine learning systems to put these models into production. Amazon also hires “Machine Learning Scientists” that focus more on theory. But most of its software engineering roles will involve some sort of machine learning. If you like building things, any software engineering role at a company that does lots of machine learning is what you’re after. Whatever they may call that role internally.
This contrasts with Data Analyst, Data Visualization, or Data Scientist roles. Those jobs focus on extracting meaning from data, generally using existing tools. As a Machine Learning Engineer, you’ll be building those tools and the systems surrounding them. A Machine Learning Engineer also is not a Data Engineer. A Data Engineer is a more specific role that focuses on collecting and transforming data before analysis. But as a Machine Learning Engineer, you’ll need a deep understanding of Data Engineering as well. The systems you build will span the entire data pipeline.
What skills do I need to become a Machine Learning Engineer?
Just knowing how to implement machine learning algorithms is not enough to become a Machine Learning Engineer. It’s all the practical stuff surrounding those models.
- The feature engineering needed to select and process that data used to train your models.
- The code needed to operationalize the models and build robust systems that run continuously.
- The distributed systems that apply these models to massive data sets.
To summarize: to become a machine learning engineer, you need to know about:
- Machine Learning / Deep Learning / AI
- Data Science
- Feature Engineering
- Software Engineering (especially in Python)
- “Big Data” and Distributed Systems
- IT Security
- Data Storage
That’s really just the minimum. But these are skills you can learn on your own – including at Udemy! A great idea is to identify companies you want to work for, go to their careers page, and search for machine learning jobs. Study the job requirements and job descriptions carefully. They will tell you exactly the skills you need to land those jobs. Understand what they are and go acquire them.
Some jobs may require more cutting-edge knowledge than others. Machine learning is a quickly evolving field. Your learning doesn’t stop once you’ve ticked off the topics above.
Don’t underestimate the importance of understanding large-scale data analytics and distributed systems. You must understand how to operationalize complex machine learning models with data that a single machine cannot process. You also must understand how to vend the results of those models at a massive scale to thousands of requests per second. Know how to horizontally scale systems using cloud computing. If you’re aiming for a job at Amazon, become an AWS expert. If you’re aiming for Google, become a Google Cloud expert. If you’re aiming for Microsoft, become an Azure expert. The machine learning piece of machine learning engineering is really the easy part — it’s doing it at a massive scale in a reliable manner that’s hard.
If you don’t want to be hands-on, a “Machine Learning Scientist” role may be closer to what you’re aiming for. But you will still need programming and hands-on skills. Scientist roles typically require advanced degrees and years of applied research experience. They are best suited for people transitioning from the world of post-graduate academia to industry.
It’s not going to happen overnight.
Formal education can get your foot in the door of the best employers. Employers often recruit directly from reputable colleges that have produced good hires for them in the past. At Amazon, Stanford, and the University of Waterloo were favorites of ours. A Master’s degree related to machine learning at one of these institutions can almost guarantee an interview before you graduate. But it is by no means an easy or inexpensive path to take. And while a formal degree helps, ultimately, employers care most about what you have built. Even for college hires, employers usually expect some sort of internship to demonstrate you can apply what you’ve learned.
Let’s have a little reality check if you’re mostly self-taught. While it’s possible to teach yourself these skills over several months, that’s not going to be enough to land you a job at a major tech employer. Knowledge isn’t enough. You need to demonstrate that you can apply that knowledge to real-world problems. It takes time to develop the experience needed to become truly comfortable with these technologies. You need experience with the sorts of problems that arise in real-world situations. It’s better to envision a journey that leads you there over time.
Initially, you’ll need to create your own experience. Practice solving problems on platforms such as Kaggle (if you become a leader there, that alone could lead to opportunities.) Take on some small contract jobs on platforms such as Upwork, and work your way up to larger jobs. The connections you make while freelancing could also lead to something larger and more permanent. Get involved with open source projects in the field of machine learning. Being able to point to your contributions on GitHub to real-world systems that are in wide use is going to look great on your résumé or CV.
The career journey of Machine Learning Engineering
You may find that this early experience doesn’t lead to a job offer at the Googles and Facebooks of the world. That’s totally okay! Launching your career at a smaller company and working your way up to larger ones is a great strategy. It can be an advantage to start at smaller companies and startups. You are going to make mistakes early in your career, and in your first job interviews. It’s a lot better to make those mistakes at smaller companies than during your one shot at the handful of larger ones!
Also, remember a Machine Learning Engineer is also a software engineer. It is easier to find jobs in software engineering than jobs that are specifically machine learning engineering. Taking a job in a more general software engineering role first will still give you valuable experience. This experience will make you more successful as a machine learning engineer. Remember, there is more to software engineering than writing code. You will need computer science fundamentals as well, including algorithms and software design. Those fundamentals can come from formal education or may be self-taught.
An even better job is a software engineer in a company that also has machine learning opportunities. It will be easier to transfer internally to machine learning engineering roles than to apply to them externally. You can think of that as sort of a backdoor into the job you ultimately want. If you take this path, get to know the hiring managers for the machine learning jobs you want to aim for. Ask to have a coffee or lunch with them, just to learn more about what their team does and the skills they are looking for. Generally, you don’t want to do this behind your current manager’s back. It’s best to be transparent about your longer-term career goals and enlist your manager’s support.
What about certifications?
Certifications tend to be more important in the early stage of your career when you might be doing freelance or contract work. Your certifications are a quick way for people to know you have some basic knowledge in the field they are hiring for. AWS continues to be the leader in cloud services. This means that the AWS Certified Machine Learning – Specialty certification may be worth obtaining while you’re on Upwork or similar freelance sites. Udemy can help you pass this exam with a prep course and practice exams.
You will find that full-time employers care less about your certifications and more about your actual experience. Think of certifications as a way to open doors to the experience you’ll need to land a full-time job later on.
Enjoy the ride!
Again, it’s a journey – this doesn’t happen overnight. But it can be a fun ride and one that might lead you in unexpected directions. At its heart is a passion for building things, including things nobody has created before. A machine learning engineer must be passionate about software engineering, programming, and technology in general. If you have that passion and the tenacity to get the knowledge and experience you need, it can ultimately lead to a very rewarding and lucrative career. Best wishes on your journey!
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