9 Pros and Cons of Being a Machine Learning Engineer

Today, artificial intelligence is no longer alien to us, especially in the era where it has become popular in enhancing productivity.

With the emerging role of artificial intelligence, the demand for AI-related jobs, typically machine learning engineers, is increasing unprecedentedly.

However, will machine learning (ML) be an ideal job for you to pursue for your future career? If you still do not have any idea, check out the advantages and disadvantages of being a machine learning engineer that I have built upon my experience!

Who Is a Machine Learning Engineer?

A machine learning engineer refers to experts who integrate software engineering skills with a deep understanding of machine learning to help optimize production and automate the data interpretation process.

Playing a crucial role in data science teams, the tasks of these engineers are to create and craft machine learning systems, apply suitable ML algorithms, and conduct experimentation.

These machine learning engineers can join various fields and industries, some emerged ones including:

  • Healthcare
  • Manufacturing
  • Marketing
  • E-commerce
  • Finance

As a machine learning engineer, you need to be versatile and can apply your skills to domains where machine learning and AI technologies are valuable.

Machine learning

Now, let’s move to our main part today: The pros and cons of being a machine learning engineer! These points are what I have summarized from my profound experience as a machine learning engineer.

1. High Demand

The number of AI companies has reached 58 thousand and one-third of the organizations have integrated AI into their process, according to BusinessDIT.

From available statistics, machine-learning jobs are projected to surge by 23% from 2022 to 2032, outpacing the average growth rate for different occupations.

From the above data, you can see that jobs related to information and computer science, typically learning machines, are still in extremely high demand. I guarantee that AI is still a prospective industry regarding job opportunities.

High demand for ML engineers

2. Impactful Technology

While machine learning pursuers are required to have a firm foundation in mathematics and calculus, individuals with IT backgrounds can smoothly transition into machine learning engineers.

3. High Salary

Due to their significant role in different fields, machine learning engineers are highly demanded by any company. So, it is no surprise that your monthly salary is high, even far from what you have desired.

Currently, the average payment for a machine learning engineer is $160,651 per year and can be more if you have many years of experience, which outweighs almost all jobs.

4. Job Security

When it comes to job security, there is no doubt that machine learning engineer is currently among the jobs with the lowest layoff rate. It can be explained by the lack of human resources in the AI sector as I have already mentioned.

If you intend to choose to become a ML engineer as your future career path, you do not have to worry as this workforce will never be enough.

5. Diverse Industries

Machine learning engineers possess a versatile skill set that outstrips industry boundaries. Their expertise in designing and implementing machine learning models allows them to solve diverse challenges across various sectors.

Whether it’s healthcare, finance, e-commerce, or manufacturing, these engineers can customize their solutions to address specific industry needs.

These superior problem-solving abilities enable machine learning engineers to adapt to emerging trends and collaborate effectively with experts from different fields.

Diverse industry for ML engineers

6. Global Exposure

As a ML engineer, I had the opportunity to collaborate with clients worldwide. Because the demand for AI implementation grows across various sectors, companies seek skilled professionals to enhance their global competitiveness.

They work on projects with international impact, such as healthcare advancements or climate modeling. Additionally, these staff often attend global conferences, networking with peers and staying updated on worldwide trends.

This global engagement not only maximizes my income but also enriches the professional growth and provides a broader understanding of the global significance of machine learning.

Complicated learning curve

7. Learning Curve

The learning curve is a drawback if you want to pursue a machine learning engineer. This is because MLEs are often required to continually learn and adapt to rapidly evolving technologies and complex machine learning concepts, which can be time-consuming and mentally taxing.

From my experience, mastering skills in big data, frameworks, Spark, mathematics, and programming can be quite challenging if you want to be a senior staff member.

8. High Pressure

ML engineers like me frequently face intense pressure due to tight project deadlines, complex problem-solving, and the expectation to deliver accurate and impactful solutions. Staying overnight to correct errors is no longer strange to me anymore.

Aside from the pressure of large workloads, you also need to deal with always updating new knowledge about AI. Otherwise, you will be left behind and hence may confront unexpected layoffs.

9. Ethical Considerations

It is undeniable that machine learning engineers have made everything become more convenient and more productive. Yet, there are still some ethical concerns about the opportunities and security of other jobs.

According to the statistics of Challenger, AI caused 4,000 job losses in May 2023, accounting for 4.9% of total job losses in this month.

These engineers also grapple with issues like bias and fairness in their models, privacy concerns when handling sensitive data, and ensuring transparency and accountability in algorithmic decisions.

The engineer considers privacy concerns

Should You Become a Machine Learning Engineer?

Deciding whether to become a machine learning engineer requires you to consider various factors.

First, consider your interest in machine learning and your passion for solving complex problems using AI technologies. Machine learning engineers need a strong technical foundation in programming, mathematics, and data analysis.

You should also possess solid problem-solving skills and be aware of ethical considerations. Do not forget to align your career goals with the demand for expertise, as it is a rapidly growing field with significant opportunities.


Here are the pros and cons of being a ML engineer that I have built on my profound experience. Before deciding whether to pursue a career as a machine learning engineer, I suggest you carefully read them.

If you are overwhelmed by the captivating advantages of this job, give yourself a chance to try it! I guarantee that you cannot stop yourself from immersing in this unprecedented experience as an engineer.