Transitioning to Machine Learning Engineer Post Graduation: Strategies and Insights

Can I Work as a Machine Learning Engineer After I Graduate?

The answer is yes, but it depends on the degree you have graduated with and the job opportunities that are available.

Note that the demand for machine learning (ML) professionals is there, but it is often hyped up and misunderstood. There is a significant gap between the hype and the actual requirements of the job market. While having a degree in machine learning is beneficial, it is not always the only requirement. Employers often seek a combination of education and experience. Most job descriptions for technical positions emphasize the need for specific levels of education and years of practical experience.

Applying Now

It is not too late to start applying for machine learning engineering roles. I myself transitioned from programming and analyzing to becoming an elite developer after 17 years of experience as a programmer/analyst. My previous work as a figure skater coach was unrelated to high-tech jobs, but my keen ability to think logically helped me enter the elite group of developers. This experience demonstrates that you don't necessarily need to have direct technical experience to transition into a machine learning engineering role.

Combining Education and Experience

Most job postings for machine learning engineering roles require a combination of education and experience. For instance, many job descriptions specify that candidates should have a 5 years of experience with a Bachelor's degree, 2 years with a Master's degree, and 0 years with a PhD. Some postings may also mention that a Master's degree is required, with a PhD being preferred.

To enhance your chances of securing a job, you should focus on selling your total package. This includes both your academic credentials and relevant work experience. Many academic programs prioritize theory over practical experience in the tools that are currently in demand. If you are not planning to pursue a PhD, consider gaining practical experience by obtaining certifications in popular platforms like AWS, Azure, Spark, or Hadoop. It is crucial to choose the right path and invest time in gaining the appropriate skills.

Continuing to pursue your education and actively seeking out opportunities to build practical experience can increase your chances of landing a machine learning engineering position. Persistence and dedication are key, and eventually, you will find the right opportunity to start your career.

Understanding the Path to Machine Learning Engineer

In my opinion, being recruited as a Machine Learning Engineer is a significant leap rather than a mere step in your career. Fresh graduates are often recruited as Data Analysts, where they are responsible for analyzing data and deriving insights from it. While data analysis is crucial, machine learning algorithms require an extensive amount of data analysis and feature engineering to reach their full potential.

Given this, my advice is to apply for a Data Analyst position. As you gain experience in this role, you will have the opportunity to transition into a machine learning engineer. This approach allows you to build a strong foundation in data analysis before diving deeper into machine learning algorithms.