How to be a machine learning engineer?

If the same question is inside your head, you are not alone!

Machine learning, Data Science, Cyber Security - are ever-growing industries that work at the forefront of the development of artificial intelligence in many industries ranging from retail, finance, healthcare, tech, and automobile.

But, these fields are relatively new, and most people have no idea what a machine learning engineer is and how to become one.

Nevertheless, you can rest assured because this article can help you by answering all your questions regarding how to become a machine learning engineer.

Here are the things you will learn from this article:

What is Machine Learning?

Machine learning is one of the most exciting discoveries in the field of technology.

And we use it every day in our daily life.

Don’t believe me?

We all use YouTube and Instagram in our free time.

You can notice that once you watch 2-3 dog videos on Youtube, the feed will automatically fill with suggestions for more dog videos.

But, how does Youtube know to suggest more dog videos?

Youtube does it by analyzing your watch history and predicting the type of content you like most. And it does everything by itself, without any assistance from a human.

Machine learning has been ingrained into our lives so well that we don’t even notice it.


In simple terms, machine learning gives our computers the ability to learn independently and without any assistance from an engineer.

How Machine Learning Works:

It starts with training the algorithm with datasets. Let’s say you want to teach the machine to understand what is a cat and what is a dog.

Then you have to start with a list of dog and cat photos which will work as data sets.

When the data sets are defined, the machine learning engineer starts to train the algorithm iteratively by inputting data into the algorithm to achieve better accuracy.

Who is a Machine Learning Engineer?

Machine learning engineer is the person behind building and training machine learning algorithms.

They are also computer programmers, but their focus goes beyond creating software and applications.

Their job is to create models that can take action without any assistance from users.

What Does a Machine Learning Engineer Do?

We’ve already established that machine learning engineers generally create and maintain self-learning algorithms without human intervention.

This sounds linear, but there are many things that go behind it.

  • Engineers have to run machine learning experiments using Python, R, and ML libraries.
  • Deploy ML algorithms into software and test workflow while working on optimizing performance
  • Perform foundational data science experiments such as analyzing data and understanding use-cases.

Machine learning engineers also collaborate with stakeholders such as data scientists, researchers, engineers, and product managers to understand clients’ requirements and product roadmap to provide a perfect solution.

ML Engineer Job Description

We are looking for a motivated and data-oriented machine learning engineer to optimize systems. You will evaluate the existing ML algorithm, perform data analysis and statistical analysis to assess data sets, and improve the accuracy of algorithms.

You should possess a deep understanding of Python, R, machine learning libraries and should have solid data science knowledge.

Machine Learning Engineer Roles and Responsibilities:

  • Consult- with stakeholders and manager to understand product requirements and objective
  • Develop machine learning systems to automate the predictive models.
  • Transform data science prototypes and coordinate with users to collect feedback
  • Test algorithm and maximum accuracy of the ML models
  • Convert unstructured data into useful information by implementing ML techniques to find patterns

Machine Learning Engineer Requirements:

  • Ability to solve complex problems, experience with multi-layered data-sets
  • Good knowledge of developing libraries and frameworks, data modeling, data structure
  • Skill to write complex ML algorithms to analyze data for making a sound prediction
  • Well versed in technical documentation
  • Machine Learning Engineer Degree: Bachelors’ degree in computer science, data science, mathematics, statistics, or any other related field
  • Minimum two years experience in ML or data science field
  • Extensive knowledge in Python, R, Java coding
  • Superb interpersonal skills with management and organizational abilities

Difference Between Machine Learning Engineer and Data Scientist?

Data scientists are primarily responsible for understanding organizational issues by gathering data and resolving problems.

The Job Description of Data Scientist:

  • Finding and removing duplicate data on data sets to maximize performance
  • Analyzing data and preparing reports for stakeholders to ensure sound decision making
  • Developing graphs, charts, and pivot tables to display data in an understandable way

Compared to the job of a machine learning engineer, which is to create algorithms that enable automatic pattern recognition and even make decisions on their own.

The Job Description of Machine Learning

  • Creating machine learning algorithms by deploying Python, R, and Java
  • Teaching ML algorithms by deploying datasets and improving prediction performance
  • Developing prototypes for submitting to stakeholders and functionality testing of software

Skills to Learn to Become a Machine Learning Engineer

There are six skills you need to master to become a machine learning engineer.

Statistics & Applied Mathematics

It’s one of the essential skills a machine learning engineer should have in his arsenal.

Statistics and mathematics have a wide range of applications in the field of machine learning. It is required to create various ML algorithms to develop statistical models for interpreting data.

Some of the essential topics you need to know are:

  • Linear algebra,
  • Probability,
  • Statistics,
  • Multivariate calculus, etc.

Computer Science and Programming

Computer science is another essential requirement to become a machine learning engineer. The more familiar you are with coding and the basics of CS concepts like data structures, data algorithms, the better you are at these, the better your chances of becoming a machine learning engineer.

Apart from that, you need to have a good grasp of programming languages like Python, R, and various libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, etc.

Machine Learning Algorithms

Machine learning algorithms are the basis of becoming an ML engineer. Here are some standard ML algorithms you should know:

  • Naïve Bayes Classifier
  • K Means Clustering
  • Support Vector Machine
  • Apriori Algorithm
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests, etc.

Data Modeling

Machine learning engineers have to work with a large amount of data daily. That’s why they need to gain an understanding of data modeling and evaluation.

The fundamental of data modeling is identifying the underlying data pattern and evaluating it to find meaningful insights.

You need to be well aware of these various algorithms to manage large datasets and speed up the machine learning process.

Neural Networks

Neural networks work like a human brain for a computer. They have multiple input and output layers. The machine interacts with the outside world with various input layers, and that information passes through multiple layers to process the information for transforming it into valuable output.

These are all the functions of neural networks. There are various types of neural networks.

  • Feedforward Neural Network
  • Recurrent Neural Network
  • Convolutional Neural Network
  • Modular Neural Network
  • Radial basis function Neural Network

You don’t need to learn about all the types of neural networks globally, but you should have a strong understanding of the core foundation of the concept to become an ML engineer.

Natural Language Processing

NLP or natural language processing is another fundamental skill you should learn before jumping into machine learning. NLP helps computers understand the nuances of human language in a better way.

You need to understand NLP libraries such as Natural Language Toolkit to work on ML applications related to language processing.

Machine Learning Engineer Qualifications

You should have a bachelor's degree in any computer science, mathematics, or related field to become a machine learning engineer.

Here are some courses you can opt for to become an ML engineer:

  • Mathematics
  • Computer Science
  • Statistics
  • Data Analysis
  • Data Science
  • Artificial Intelligence

You can get a master’s degree in a more advanced field such as

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks
  • Deep Learning
  • Big Data

Google Machine Learning Engineer salary

  • According to Glassdoor, a typical Google machine learning engineer salary is $1,42,568 per year

  • According to, the maiden salary of a Machine learning engineer is around $141,214 in the USA.

  • And according to PayScale, here is the salary graph for an entry-level to an experienced ML engineer.

If you look at the below trend chart, you will see that the value of a machine learning engineer is ever-increasing, and you can make a lot of money if you jump into this career early.


Machine Learning Engineer Career path

Here is a representation of a typical career path of a Machine Learning Engineer:




We hope this blog has helped you learn more about how to become a machine learning engineer. However, here are some of the key takeaways you can get from this blog.

  • Learn machine learning skills such as neural network, NLP before jumping into a machine learning job.
  • Get experience in machine learning with personal or open-source projects or internships.
  • Apply for machine learning jobs with a stellar machine learning resume.

If you have any more questions, please leave us a message at, and we will get back to you as soon as possible.