Is Machine Learning Harder Than Software Engineering?

If you are thinking about a career in computer science, software engineering and machine learning are two possibilities. Engineering a machine to think sounds rewarding and challenging. Since you will be investing time and money into learning new skills, it is reasonable to ask how difficult machine learning engineering will be.

Machine learning is not harder than software engineering, but it requires a different mindset. A software engineer writes rules for a computer to automate a task. A machine learning engineer feeds computer data and algorithms, and the computer creates the rules. 

Read on to learn how the two are different and what it takes to become a machine learning engineer.

Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!

What Are the Differences Between Machine Learning and Software Engineering?

A software engineer writes programs that computers can “read” to automate tasks for us. The engineer creates a set of rules the computer or program uses to complete the task. If you have been asked to approve an email because it wasn’t in your contacts, that could have been a rule a software engineer wrote. 

The machine learning engineer’s goal is to get the computer to create the rules to automate the task. Instead of coming up with a series of commands, the engineer feeds the computer the results, creates an algorithm for the computer to use, and then has the computer create the rules. 

In the email example, the AI engineer would feed the computer data–millions of emails and which were spam, along with machine learning algorithms or instructions that the computer should use to come up with its own rules. The goal is to have a computer figure out that the spam marketers have changed strategies and adjust their behavior accordingly.

Essentially, a machine learning engineer tries to get a computer to do a particular task without human intervention. When an individual asks Alexa to do something, Alexa needs to respond appropriately, and the machine learning engineer develops the code that tells Alexa how to interpret the command.

Machine learning engineers cannot do this alone, however. The data scientists help by sorting through the enormous data sets, and the software engineers write the code needed to make this happen. 

Do Machine Learning Engineers Make More Than Software Engineers?

Given that salaries are dependent on experience and companies and change based on demand, these figures are not set in stone. However, according to Glassdoor, the average salary for a software engineer in the United States is currently $92,046, with a low of $62,000 and a high of $134,000.

According to Glassdoor, the average yearly salary for a machine learning engineer is currently $114,121 a year, and the ranges between the lowest and highest are also consistently higher.

Other sites list different average salaries. Indeed claims a machine learning engineer has an average salary of $146,146 while a software engineer makes $107,408 annually. 

Even though sites list different salaries, they consistently show that a machine learning engineer makes more money. But why is that?

Demand for Machine Learning Engineers Is High

AI and Machine Learning engineers are in high demand right now because AI is becoming a more significant part of our lives. Computer technology is changing so rapidly, and the amount of data is increasing exponentially that we cannot rely solely on software.

Also, because the demand for machine learning is high, engineers can ask for higher salaries or go work for a competitor. Although the higher salaries suggest that machine learning is more challenging, supply and demand could be the driving force behind machine learning salaries. Or it could be that machine learning is more difficult.

Is Machine Learning Difficult?

Software engineers, data scientists, and machine learning engineers must have strong math, statistics, and computer skills. They must also have analytical skills. Anyone who does not have these skills will find machine engineering difficult. But they would also find data science and software engineering challenging.

So if you do not have a solid grasp of complex mathematics and do not know how to write code, you should work on that first and then learn machine learning. But the same is true if you want to be a data scientist or a software engineer.

Software Engineers Need a Different Mindset

What makes machine learning difficult for software engineers is it requires a different mindset. 

A software engineer starts with a set of rules, observes what the machine does, and then adjusts and tweaks the rules to always get the desired outcome. Although it is something of an overgeneralization, the process is far more linear than what a machine learning engineer does.

The machine learning engineer focuses on adjusting the algorithm and seeing how the computer responds. It is a messier process that requires the engineer to be more open to experimentation.

Imagine giving someone directions to get from one place to another. The software engineer is thinking about giving clear, specific directions on how far to go before turning, which direction to turn, and so on. If the directions don’t work, the engineer rewrites them. 

Now think about teaching a kid to ride a bike. The machine learning engineer gives guidance to the kid and observes what the kid is doing. If the kid is not successful, then adjustments in directions are required.

The Engineering Roles Are Often Different

As you can see, often, the roles of the two are different, especially in larger companies. A software engineer’s responsibility is often related to a company’s infrastructure. The machine learning engineer, however, focuses on the customer’s experience. If a website has a new feature, it was likely written by a software engineer. 

If the site gives you a recommendation for something it thinks you will like, the algorithms that taught the site to make the recommendation was probably designed by the machine learning engineer. A good prediction means the computer adapted to the user, but a poor prediction means the algorithm needs to be tweaked.

Can You Learn Machine Learning on Your Own?

Yes, you can learn how to engineer machine learning. But you need to think about where you plan to work with your knowledge. Do you want to work for an established firm, a medium-sized company, or a start-up? 

  • Large companies. Large companies look for people who have a 4-year degree at a minimum. Certifications without the degree will not impress them. Having substantial evidence that you have the required skill set will.
  • Medium companies. Degrees are less important, but certificates can be useful. Experience, aptitude, and flexibility are traits they find valuable.
  • Small companies and start-ups. Small companies and start-ups often do not clearly understand the differences between a software and a machine learning engineer. They will tell you their goals and expect you to make them happen. Working for small companies requires you to be able to wear many hats.

No matter the company’s size, flexibility, interpersonal skills, being a team player, and being a problem solver are essential skills.

How Long Will It Take to Learn Machine Learning?

If you plan to go the route of a degree, it will take 3-4 years to get a B.A. or 1-2 years to earn a Master’s. An IT degree from a community college can give you some necessary computing skills if you need those. But if you plan to teach yourself, how long it will take depends, at least in part, on you.

The key to teaching yourself machine learning is simple, though—projects. Courses on Codecademy, Coursera, and Udemy are valuable—if you use your knowledge for projects. 

  • Use Spark to create an app
  • Take project heavy Udemy courses like Data Analysis and Complete Python Bootcamp
  • Create competitions on Kaggle

Remember that small- and medium-sized companies will want to see what you can do, not whether you have a Udemy certificate. 

Author’s Recommendations: Top Data Science Resources To Consider

Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.

  • DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
  • MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
  • Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.


Machine learning programming is not harder than computer engineering—it requires a different skillset and mind frame. Instead of writing rules for a computer and then adjusting the rules as needed, programming a machine to learn requires that the computer have a lot of data and algorithms to create the rules. 

BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.

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Daisy is the founder of Passionate for the field of Data Science, she shares her learnings and experiences in this domain, with the hope to help other Data Science enthusiasts in their path down this incredible discipline.

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