Why do Self-Taught ML Engineers Take Many Years to Land a Proper Job?



Self-taught ML engineers

Self-taught ML engineers have to wait patiently with a passion to get recruited by tech companies

The growth of the machine learning field is exponential in the global tech market. The combination of artificial intelligence and machine learning has taken over the technical domain through AI and ML models with smart products and services. ML jobs are gaining popularity and are in high demand for being a vast area with frequent new developments for customer satisfaction. There are two categories of candidates applying for ML jobsself-taught ML engineers and ML engineers with a degree. But self-taught ML engineers have to struggle much more than ML engineers with a degree. Yes, even if companies in the FAANG group have started accepting self-taught engineers in different fields, it still takes more time for self-taught ML engineers to get accepted and receive an offer letter with lucrative salary packages per annum. There are plenty of ML jobs available in the market but it is easier for ML engineers with machine learning degrees or a Master’s/Ph.D.

There are several crucial steps to gaining different perspectives of machine learning for self-taught ML engineers. Multiple principles of mathematics, coding, data management, and many others are there to make machine learning algorithms work properly in ML models. Self-taught engineers have a larger scope but a difficult path to have a deep understanding of the vast machine learning field to apply for reputable ML jobs. Yes, ML engineers with degrees are reaping the benefits of reputed educational institutions, their faculties, as well as the placement offers. They know just much the syllabus tends to cover machine learning as well as hands-on experience on ML models.

But for self-taught ML engineers, have to search for different ways to level up their knowledge and brush up their technical and analytical skills than these ML engineers with a degree. There is a wide difference between enrolling in an engineering course and covering as much as possible through research. There are some tech companies ready to loosen the qualification criteria of the education requirements. Then, there are a plethora of opportunities for self-taught ML engineers to apply for machine learning jobs and kickstart their successful careers.

Meanwhile, some companies hire machine learning engineers with more than five years to ten years of work experience in multiple projects. This can become a difficult terrain for self-taught ML engineers to look out for ML jobs and work on ML models. They should work harder to reach the level of around ten years of experience because there may be a restriction from companies to hiring ML engineers without a degree. It is a time-consuming process to look out for companies interested in hiring freshers without any professional degree. This creates the path to impress present employees with the necessary skills and knowledge of the technical field.

There are different ways to gain knowledge of machine learning for self-taught ML engineers to grab ML jobs from ML engineers with a degree such as own personal projects on GitHub, continuous participation in machine learning competitions on Kaggle, enrolling in online educational platforms such as Coursera, LinkedIn, edX, udemy, and many more.

Yes, self-taught ML engineers have many ups and downs through challenges but it takes more strength and passion than those who are with a degree. They have to stick to the proper training part to not fall short of their passion for a learning experience. Thus, it may take a roller-coaster ride to land ML jobs in global tech companies without a degree. It is recommended to not lose hope and have the dream to contribute to the machine learning field as self-taught ML engineers.

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