機械学習エンジニアが「やめとけ」と言われている理由とは?機械学習エンジニアの魅力も紹介Why are Machine Learning Engineers Told to Stop? Also, what makes a machine learning engineer so appealing.

Many people have the impression that being a machine learning engineer is a glamorous profession with high income and developing AI. On the other hand, however, there are also negative opinions flying around the Internet saying, "Don't become a machine learning engineer.

In this article, we will explain in detail why machine learning engineers are being told not to become a machine learning engineer. We also introduce the attractiveness of machine learning engineers and the characteristics of those who are suited and unsuited for the job.

Machine Learning Engineer is a technical position in AI development, especially in the implementation and development of machine learning (Machine Learning). Machine learning is one of the AI technologies that learns its own rules by studying a huge amount of data, and makes predictions and decisions in accordance with those rules.

At first, AI is nothing more than a simple computer that knows nothing. However, by providing it with large amounts of data and appropriate learning methods, it can understand the structure and characteristics of the data and automatically perform tasks such as prediction and classification based on rules set by humans. The role of a machine learning engineer is precisely this: to give AI learning and make it smarter.

Predictive models and information processing techniques using machine learning are used in various fields such as manufacturing, retail, medicine, finance, insurance, and marketing, and the demand for machine learning engineers is rising every year accordingly. Let's take a closer look at the specific duties of machine learning engineers in the following section.

Main Duties of a Machine Learning Engineer

Machine learning engineers are responsible for a wide range of tasks, from data collection and processing to algorithm and model development, service design and development, and infrastructure construction, operation, and maintenance.
 

• Collection and processing of data necessary for machine learning
• Development and validation of machine learning models
• Design and development of systems using machine learning
• Construction, operation, and maintenance of infrastructure for machine learning
• Research and investigation of the latest technologies

Among these, the main task is to develop algorithms and models by having AI read large amounts of data and discover features and regularities in the data. Through this kind of work, the main role is to drive innovation in a wide range of fields, including natural language processing, image recognition, speech recognition, and future prediction.

In addition to work related to models and algorithms, machine learning engineers also collect and process data, design and develop systems on which AI runs, build infrastructure, and develop additional functions.

Here are five reasons why machine learning engineers are told not to do it.
 

• It takes time to learn the technology.
• High expectations from the company
• It can be hard work
• Competition is expected to increase
• Must keep up with the latest technology

It takes time to master the technology

The first reason is that it takes time to learn the technology. Compared to other engineering positions, the amount of knowledge and technology to be acquired is vast, and this is not an easy position to pursue.

There are many things to learn, including machine learning algorithms, libraries, and frameworks, not to mention knowledge of programming languages such as Python and R, which are necessary for model development. Moreover, each of these is difficult to learn, and it takes a considerable amount of time and effort to reach a level where you can play an active role as a full-fledged engineer.

Because of these hurdles, many people fail in the middle of their studies. And it is thought that those who have witnessed the harshness of the training have negative comments such as, "Don't do it.

High expectations from the company

Another reason why negative opinions about machine learning engineers are rife is the high expectations required of them by their companies.

Machine learning is a field that has been attracting a lot of attention in recent years, and many companies are actively considering introducing machine learning. However, many companies do not have the know-how or systems in place to train machine learning engineers.

In addition, machine learning engineers do not simply follow instructions. They need to work while considering "how best to develop" after listening to the client company's problems and requests. Therefore, sometimes the answer you derive may affect the future of the client.

Thus, not only high technical skills but also problem-solving, logical thinking, and consulting skills are required, and many people feel pressured to do so.

It can be hard work.

The third reason is that it is easy to become hard work due to the shortage of human resources. In recent years, with the dramatic development of AI technology, the demand for machine learning engineers has skyrocketed. Although the number of people who want to become machine learning engineers is increasing every year, the supply is not keeping up with the demand because it takes time to train them.

Against this backdrop, work tends to be concentrated on individual engineers and the workload is more strenuous than in other occupations. If there are changes in system specifications, overtime and holiday work may be required to meet deadlines.

Furthermore, AI is still in its infancy, and new technologies and methods are emerging frequently. To keep up with these changes, they must study independently outside of work hours and update their skills.

Thus, machine learning engineers are hard workers, and this job is not for those who "want to avoid working overtime" or "don't want to work on their days off.

Competition is expected to intensify.

In recent years, interest in machine learning technology has been growing, and the number of people who want to become machine learning engineers is increasing. At present, there is a shortage of IT personnel, but this situation will not necessarily continue into the future.

Revisions to the curriculum guidelines by the Ministry of Education, Culture, Sports, Science and Technology have led to the start of programming education in elementary schools, and universities are opening machine learning-related departments and courses one after another. As a result, we can expect to see an increase in the number of highly skilled young people and engineers changing careers from other fields in the future.

As the number of such personnel increases in the future, it will become difficult for personnel who have mastered only basic skills to differentiate themselves. As competition becomes more intense, the opportunities for success may become more limited.

You must keep up to date with the latest information.

AI technology is advancing daily, and new trends and technologies are constantly emerging. It is not uncommon for a technology that was mainstream a few years ago to be outdated today. Conversely, technologies that were largely unknown before may develop rapidly.

Deep learning, for example, has experienced a rapid boom in recent years and has become one of the essential technologies in the field of AI. In order to keep up with these changes, one must keep up with the latest information and update one's skills. However, keeping up with the latest information can easily become a time and mental burden for machine learning engineers.

While some may say, "Don't become a machine learning engineer," on the other hand, machine learning engineering is an occupation that has many attractions. Here are six of the most attractive aspects of being a machine learning engineer.
 

• Compensation is high
• Good future prospects
• Able to work in a wide range of fields
• Many projects are available for freelancers
• Can work overseas
• Shortage of human resources makes it possible to change jobs with high compensation.

Compensation is high.

Machine learning engineers are highly specialized and difficult jobs, so one of their attractions is that they can expect to receive high compensation. In fact, if you search for "machine learning engineer" on job information websites, you will find many jobs with annual incomes of 6 million yen or more.

Job Page of Recruitment Agent "United World Inc.

Job Page of Recruitment Agents "United World Inc.

Although annual salaries vary depending on the region, industry, and skill level of the individual, experienced and technically skilled machine learning engineers can earn more than 10 million yen per year. Below are actual posted jobs with annual incomes of 10 million yen or more.

Job page of career change agent "United World Inc.

Job page of recruitment agency "United World Inc.

If you have unique skills that other machine learning engineers do not have, or if you specialize in a particular field and have developed expertise, you may be able to earn even higher compensation.

Future Prospects.

The market value of machine learning engineers is not expected to decline significantly in the future, as AI technology-based systems and services are spreading at an alarming rate, and the number of companies considering their introduction continues to increase every year.

As a result, machine learning skills are increasingly in demand, and the demand for highly skilled machine learning engineers is growing steadily.

The field of AI will continue to expand in the future, so if you can keep up with the latest trends and improve your skill level, you will have no trouble finding a position in the field.

Active in a Wide Range of Fields

Machine learning engineers are in demand not only in IT companies but also in a wide range of industries.

For example, AI is used in manufacturing for quality control and productivity improvement, in the medical field to improve the accuracy of diagnosis, and in the financial field for risk management and fraud detection. The use of AI in these fields is expected to continue to expand, and machine learning engineers will be able to play an active role in a wide range of fields.

The number of job openings in all industries continues to increase, and the range of options is wide, so it is possible to choose a job in a genre in which you excel. By taking into account your interests, strengths, future prospects, and working style, you can build a more fulfilling career by finding a field that suits you.

Many projects are available for freelancers.

One of the most attractive aspects of being a machine learning engineer is the increasing number of freelance opportunities available. In recent years, machine learning engineers have tended to be in higher demand than other engineering jobs. This is due to an increase in the number of projects related to AI, such as deep learning, which is attracting worldwide attention, as well as the increasing shortage of engineers in the IT market, which has led to a rise in the job market.

As a result, freelance machine learning engineers are finding it easier to take on projects with the unit prices they desire. As long as there is a shortage of human resources for machine learning engineers, the number of projects for freelancers is expected to continue to increase, which will be a great attraction for those who wish to work as freelancers in the future.

Work can also be done overseas.

AI technology is attracting attention not only in Japan but around the world, and demand for machine learning engineers is increasing in companies around the world. AI-related jobs offer relatively high annual salaries, so you can expect to earn an even higher income than in Japan.

For example, according to the U.S. job search site Glassdoor, the average annual salary for a machine learning engineer as of May 2024 is $166,000. Converting this into Japanese yen at the same exchange rate (153 yen to the dollar), the amount would be more than 25 million yen, showing that the market is considerably higher than in Japan.

Reference: Glassdoor "Machine Learning Engineer

Please refer to the following article for an explanation of the characteristics of foreign-affiliated companies.

Related: How to Work at a Foreign Company Differences from Japanese companies, suitable candidates, qualifications, and educational background are explained!

Shortage of human resources makes it possible to change jobs with high benefits.

The demand for machine learning engineers continues to increase year after year along with the advancement of AI technology, but the shortage of IT personnel in Japan remains unresolved. In particular, the supply of human resources with expertise in machine learning has not yet caught up with the demand, and companies are all looking for talented engineers.

In order to solve this shortage, companies are actively trying to hire machine learning engineers with high compensation.

As mentioned above, there are many jobs in this category that offer annual incomes of 10 million yen or more, so it is possible to aim for a high paying job change by making the most of your skills and experience. In addition to annual salary, an increasing number of companies are offering attractive conditions such as promotion and skill improvement opportunities and excellent benefit packages, making it easy to find a position that matches your career and conditions.
 

Many people are considering changing careers to become machine learning engineers because of the bright future and high annual salary. However, there are some people who are not sure if this is really the right job for them and want to know the characteristics of people who are not suited for it.

So here are three characteristics of people who should not become machine learning engineers.
 

• People who are only interested in high income
• People who do not feel curiosity about technical improvement
• People who cannot work while being aware of the challenges that lie ahead in AI development

People who only aim for a high income.

Machine learning engineers can expect to earn a high income, but those who only aim for a high income should not apply. The basic premise is that you need to learn a lot to become a machine learning engineer. In particular, a wide range of knowledge is required, including mathematics, statistics, programming, data analysis, and machine learning algorithms.

These are not easy to learn, and without strong will and high motivation, it is easy to fall behind. Therefore, those who only aim for a high income will not be able to maintain their motivation along the way and are more likely to give up before acquiring the knowledge and skills required for this type of job.

In addition, as we have already mentioned, the work of machine learning engineers tends to be more strenuous than other types of jobs, and it is not uncommon for overtime and holiday work to increase. And no matter how busy they are, they also need to study on their own after work and on their days off to keep up with the latest AI technologies. If you are not prepared for this kind of hard work, you will find it "tough.

People who do not feel curiosity about technological advancement

Machine learning engineers have to keep updating their skills as AI technology advances. Therefore, if you are not curious about new technologies, you will not be able to keep up with the changing times and may be left behind.

If you have curiosity about technological advancements, you will tend to enjoy creating your own solutions by utilizing new algorithms and incorporating knowledge from other fields. However, if they lack curiosity, they will stick to the current way of doing things and find it difficult to incorporate new technologies.

As a result, the skill level gap between them and those around them will widen, which may lead to a situation where they are unable to earn the salary and benefits they desire.

People who cannot work with an awareness of the challenges that lie ahead in AI development.

Machine learning engineers are expected not only to solve technical problems, but also to understand the impact of AI technology on society and ethical issues, and to approach their work with a broad perspective. For example, it is important to design with an awareness of issues beyond AI development, such as ethical decision making and privacy protection.

If you cannot work with an awareness of these issues beyond AI development, unfortunately, you are not a suitable candidate for a machine learning engineer.

AI technology must not only work, it must be used in an ethical and socially responsible manner. AI development that lacks ethics may ignore the potential risks posed by the technology, ultimately leading to a loss of public trust.

There are three main characteristics of a person who is suited to be a machine learning engineer
 

• People who can think logically and are good at mathematics
• People who are comfortable learning, investigating, and researching
• People who are self-disciplined

Let's take a closer look below.

People who can think logically and are good at mathematics

Among engineering positions, machine learning engineers in particular require a high level of mathematical knowledge and logical thinking. Understanding mathematical concepts and the ability to handle mathematical formulas are essential, as they design algorithms and evaluate models based on mathematical knowledge such as statistics and probability theory.

The ability to think logically about things, such as "What inputs are needed to solve this problem?" and "In order to obtain these inputs...," and to work efficiently is also required. This ability to think logically is also important for verification in the development process.

Therefore, people who are good at mathematics and like to think deeply about things have a high aptitude for this type of work.

People who are comfortable with learning, investigation, and research

Another important aptitude for becoming a machine learning engineer is whether or not you are comfortable learning, investigating, and researching, and whether or not you are eager to continue learning new things. Again, because of the rapid pace of technology updates in the field of AI, in order to be active on the cutting edge, you must be willing to constantly follow and learn the latest technologies and knowledge.

In particular, machine learning algorithms and tools are evolving rapidly, and what is currently mainstream may be replaced by another technology in a few years. Under these circumstances, people who are curious, keep up with the latest information, and are willing to learn new knowledge and technologies are well suited for this position.

By learning new knowledge and techniques, you will be able to solve more advanced problems and grow as an engineer.

People who are self-disciplined

Due to the chronic shortage of IT personnel, many companies have only one machine learning engineer in their company. As a result, you may have to handle multiple tasks alone, such as data analysis, algorithm/model development, and infrastructure construction.

In addition, projects are often long-term and may hit a wall along the way. Even under such circumstances, you need to systematically manage your schedule, maintain motivation, and finish the project to the end.

Therefore, those who can manage themselves in various aspects are likely to be successful as machine learning engineers.

In this article, we have explained why machine learning engineers are being told to "stop" and the characteristics of those who are and are not suited for machine learning engineering.

Many people may be thinking about changing careers to become machine learning engineers in the future, as it has been attracting attention as a profession with bright prospects.

While it is an attractive profession that offers no shortage of opportunities as long as you have the skills, it is also true that the skills and qualifications required are high due to the high degree of specialization. If you enter the field without the right aptitude, you may end up falling behind.

Therefore, it is important to carefully consider your own aptitude before deciding whether or not to become a machine learning engineer.

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