Everyone knows machine learning is a hot skill and a lucrative one. According to Glassdoor, the average base pay for a machine learning engineer 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.
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.
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.
To summarize: to become a machine learning engineer, you need to know about:
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.