Welcome to this article in which we will discuss the professional figure of the Machine Learning Engineer.
More specifically we’ll see if this professional figure is suitable for what you want to do, as well as see who he is, what he does, what tasks performs a machine learning engineer, how much he earns and what are the differences between a data scientist and a machine learning engineer.
Before we start, as always, I want to tell you why I’m writing this article.
The field of Artificial Intelligence and many of the professions inherent to roles such as Data Scientist, Data Engineer, Machine Learning Engineer or AI Developer, only now are growing.
The downside of the matter is that companies as well as people themselves, are not well able to understand what differentiates each of these professional figures.
Some of the most frequent questions that a company asks itself are: “Do I really need these figures for my business?”, “What functions do they cover?”.
That’s why to this day I’m writing this article. I want to help you and the companies that will read it understand what this figure does.
Like any of my articles, I recommend you read to the end to fully understand the topic at hand.
Are you ready for this amazing dive?
Then let’s get started!!!
P.S. In order not to be repetitive in this article I will call the Machine Learning Engineer in different ways (ML Engineer or MLE). Enjoy!
What is or rather who is a Machine Learning Engineer?
The MLE is a mythological figure…no lie, just kidding.
The Machine Learning Engineer is a professional figure, an expert programmer who deals with Machine Learning projects and more generally with Artificial Intelligence.
In other words, his task is to make sure that the implementation of the algorithms designed by him and/or the team, are applied correctly for ready use in reality.
So, an ML engineer builds and applies Artificial Intelligence (AI) systems that leverage huge datasets, but let’s get more specific.
ML Engineer what do you do, what are your responsibilities?
A Machine Learning Engineer can take care of several different projects depending on the company they work for. But if we go to see from the top (and therefore more generally) this type of figure, we can see the main characteristics and functions performed by those who take on this role.
- Design, development and research of Machine Learning models
- Research and selection of appropriate datasets
- Performing statistical analysis and using the results to improve the models
- Re-engineering ML systems and models based on business needs
- Identifying differences in data distribution that could affect model performance in real-world situations
- Understanding when results can be applied to business decisions
- Enriching existing ML frameworks and libraries
Broadly speaking, this list represents the tasks that a Machine Learning Engineer performs in an enterprise. Even if the list seemed long, don’t freak out, these tasks are usually done at specific times in the project.
The Hard Skills that make up an MLE
AI is a really vast field and when a company decides to hire or form an Artificial Intelligence team, they don’t really know which figures to assign a specific task to. Unfortunately, this is to the detriment of the figure working in the Artificial Intelligence field because once he/she joins the company, he/she finds him/herself covering multiple roles that do not fall under that profession.
As well as the Data Scientist, the Machine Learning Engineer also has his hard skills required:
- Programming in Python and C++
- Knowledge of mathematics and statistics
- Knowledge of Machine Learning and Deep Learning to follow different projects
- Know how to use SQL language to manipulate the database
- Know how to use Flask
These are the most important skills to get started (I’ll recommend some courses later), but obviously they are not enough. The ideal person must also have the so-called “soft skills” such as: being able to work in a team, the ability to manage projects, know how to organize etc..
Ok, but let’s get down to business and see how much you earn doing this job?
How much does a Machine Learning Engineer earn?
As you could read until now, the ML Engineer is a figure that like the Data Scientist has many responsibilities in a company. That’s why his salary is no less, in fact, this is one of the highest paid figures in the AI world.
His gross annual salary can vary depending on his experience, skills, tasks performed and the company he works for, but more generally, here are some price ranges you can stick to.
In America, this Machine Learning professional earns an average salary of $140k per year, but they can also earn figures around $170k per year. In the city of San Francisco even there are MLEs who earn $200k.
If you have read the article on “Who is a Data Scientist” you will know very well that this last figure earns much more, but let’s see some more substantial differences between the two.
Machine Learning Engineer vs Data Scientist
Both of these figures are important for companies, so the presence of one in the company, should not exclude the other. Although at times they are very similar due to their freshness (they were born recently), in reality they are not and there are tasks in particular that differentiate them.
What mainly differentiates the two roles are two characteristics in particular:
- The Data Scientist (DS) is concerned with what direction the business needs to take. That is, in the most specific case is the person who after analyzing the data and developing an algorithm, suitable for the problem and the business, must communicate with the company’s executives to indicate the most “right” way according to the predictions made. The Data Scientist must have business skills.
- The Machine Learning Engineer, unlike the DS, is the one who deals with the implementation of the algorithms with business software and for customers. This figure must be experienced in implementation.
Both have skills in common, but also complement each other. For example, the DS must be particularly good at data analysis, mathematics, and statics, but most importantly, they must have an understanding of the business in order to make the right business decisions.
An MLE, on the other hand, must have useful skills in algorithm implementation, database management, and other characteristics described above.
But how does one become a Machine Learning Engineer?
If you want to build the skills to become an ML Engineer you have to work hard and study a lot.
As of today, it is believed that you necessarily need a college degree to do this kind of profession. Well my friend, you must know that this is bullshit!
We are not talking about being a doctor.
(attention: I don’t want to discredit a degree or a computer science, mathematics or statistics university path)
Nowadays with the advent of the famous internet there are several methods to acquire such skills, demonstrate them and become an MLE.
One of the best methods you have to become proficient in the field is to follow the experts in the field.
If you lack the basics, I recommend reading this article that gives you a complete path to study and understand artificial intelligence.
Otherwise here is a list of courses you can take in the respective order: Python, Data Visualization, Machine Learning, Deep Learning, Flask.
Starting this path is not easy, but if you intend to go down this road I suggest you focus, take your time and do things right. Also to find work in the ML field there are many places you can look. The most popular ones are Linkedin, Indeed, Glassdoor and Upwork.
Having said that, I hope I’ve made you clearer about the ML Engineer and that you liked the article, write in the comments or contact me if you want to ask for more information.
Until next time!