Interested in a project like Netflix’s movie/show recommendations, self-driving cars, image/speech recognition or disease prediction? A career as a machine learning engineer might be for you!
Even if you are not yet looking to pursue a career in machine learning, learning about roles can help you decide if this career path is right for you. Also, even if you have little tech experience, having your dream career in mind can motivate you as you learn to code and keep you on the right track.
So, what is a machine learning engineer? What do machine learning engineers do? What is the average salary for a machine learning engineer?
In this post, we will cover what machine learning engineers do, why ML skills are in high demand, the difference between data scientists and machine learning engineers, available machine learning jobs, machine learning salaries, and requirements for ML skills. machine-learning engineers and more.
Disclosure: I am a proud affiliate of some of the resources mentioned in this article. If you purchase products through my links on this page, you may receive a small commission in return for my recommendations. Thank you!
What is a machine learning engineer?
Machine learning engineers are essentially computer programmers who focus on researching, designing, and building self-learning applications and software.
🧠 Machine learning engineers “train” computers and software to learn on their own with minimal human supervision.
As a profession, machine learning is very interdisciplinary. That is, it is derived from a variety of disciplines, especially software engineering and data science. Learn more about what machine learning really is.
Because ML engineers “teach” systems and software to learn from vast amounts of data, machine learning engineers clean and optimize data, understand data models and data structures, query data sets, analyze data and present use cases, and more.
Data Scientist vs Machine Learning Engineer
Machine learning engineers often start out as data scientists, but they definitely have different roles, so let’s quickly break down the jobs of machine learning engineers and data scientists.
Data scientists tend to focus on statistical analysis of data (i.e., collecting and interpreting data to find patterns and trends), use that analysis to determine which machine learning model to use, and then define and prototype that model. .
They then pass this initial work on to machine learning engineers who write code to make everything work in a large production environment (i.e. where users and/or other software can use it). This is just one of the ML engineers’ responsibilities and the way they work with data scientists.
Here is Snapchat’s machine learning engineer job description to give you a better idea of the responsibilities associated with machine learning engineer jobs.
Another machine learning engineer job description at Apple:
Don’t worry if this is all Greek for you now. Get your skills and knowledge of data science and machine learning and we’ll be with you!
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Average Machine Learning Engineer Salary
💰 Machine learning engineers are the highest paid people in technology. In fact, the average salary for a machine learning engineer is a whopping $150,917. Salary can go up to $3 million or more! (For reference, the average salary for a data scientist is $119,647 and a software engineer is $104,108.)
Machine learning is AI, machine learning, data science, and software engineering.
Sky-high salaries can raise averages, so let’s look at typical machine learning salaries based on experience level.
- average beginner’s salary For machine learning engineers $82,941 per year
- NS middle level Machine learning engineers with 5-9 years of experience can count on a return. $112,095 per year
- and difference What ML Engineers Can Get $155,431 per year
It takes time to climb the ladder as an ML engineer, but you are already starting at a fairly high level! But keep in mind that basically there are no true beginner ML engineers. Almost always, you must first have experience in other technical roles.
Job Prospects and Demand for Machine Learning Engineers
📈 According to the Future of Jobs Survey 2020, AI and machine learning experts rank second for growing demand after data analysts and scientists.
Many experts claim that the COVID-19 pandemic has accelerated job growth in AI and machine learning as more and more tasks (e.g. robots delivering pizzas and packages, contactless payments) are automated.
According to PwC’s annual Global CEO Survey, 85% of CEOs say AI will significantly change the way they do business over the next five years.
According to Forbes, AI and machine learning expertise will be the fastest-growing technical job skills in 2021 and beyond. Furthermore, machine learning jobs are expected to be worth nearly $31 billion by 2024.
All of these studies show that machine learning is a fast-growing and in-demand profession, especially as more and more companies are using data to accelerate their growth. A great time to start working on becoming a machine learning engineer!
How to become a machine learning engineer
So how do you actually become a machine learning engineer? 👇
Many machine learning engineers have master’s degrees or doctoral degrees in quantitative fields such as computer science, data science, mathematics, or statistics. (but you don’t necessary Entry level to machine learning)
Often, machine learning engineers leave similar roles such as software engineering or data science. Some machine learning engineer job postings require 2-4 years of professional experience as a software engineer or data scientist to fill the role. Starting in one of these roles can be a great way to advance into the field, especially if you are self-taught. Here’s how to become a software engineer without a CS degree.
In addition to experience, you will generally need to know the following skills to qualify for machine learning jobs:
- Coding in Python, Scala, Java, R and/or Julia (at least one)
- Statistics, Linear Algebra, and Calculus
- Machine learning framework (scikit-learn, H2O)
- Machine learning technologies (NLP, computer vision, decision trees, transformers, clustering algorithms, etc.)
- Deep Learning Frameworks (TensorFlow, PyTorch)
- Software engineering/computer engineering skills (design patterns, algorithms, etc.)
- Data Science/Analysis Technology
Ultimately, if you’re hoping to become a machine learning engineer right away from an online course or bootcamp, that’s not exactly what happens because machine learning isn’t a beginner-friendly profession. Relevant roles usually require several years of experience or advanced academic experience (master’s, doctoral).
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Where can I learn machine learning?
Taking a machine learning course along with a data analytics/data science course can be a great way to prepare you for the machine learning engineer career of the future!
👉 Check out the 13 best machine learning courses to help you get started. There are options for Coursera, Pluralsight, edX, and more.
Is a machine learning job right for me?
If you already have a background in coding, data science, and/or math, you are off to a great start to becoming a machine learning engineer.
But even if you are a complete beginner, it can be a great ultimate career goal for you to get the job done. If you are passionate and excited about the field, it is a dream you should follow!
Overall, a job as a machine learning engineer can be perfect if you love working on cutting-edge projects, enjoy the challenge, and want to do something that has the potential to change the world. Commitment and dedication to get there.