10 Reasons Why Machine Learning Engineers Should Change Jobs in 2023

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. As machine learning jobs can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer. This is the most important reason why machine learning engineers should change jobs in 2023 and can give try to some new skillset to secure their careers. There are multiple reasons not to become machine learning engineers and the top one is that it is pretty not easy to master Machine Learning. Also, machine learning engineers should change jobs as it doesn't help in this economy. Here the article will focus on 10 reasons why machine learning engineers should change jobs in 2023.

It takes time and resources for machine learning to yield tangible results

Machine learning occurs over time. So, there will be a period when your interface or algorithm won't be developed enough for your company's needs. The precise amount of time required will depend upon the nature of the data, the data source, and how it is to be used. You'll simply need to wait as new data is generated — sometimes this can take days, weeks, months, or even years!

Machine Learning will transition to a commonplace

Machine Learning will transition to a commonplace part of every Software Engineer's toolkit.

The Machine Learning Engineer role is a consequence of the massive hype fueling buzzwords like AI and Data Science in the enterprise. In the early days of Machine Learning, it was a very necessary role. And it commanded a nice little pay bump for many! But Machine Learning Engineer has taken on many different personalities depending on whom you ask. Now top tech companies are not clear about what Machine Learning Engineer means to them. This may put the Machine Learning professional in dark.

Machine Learning Engineer is necessary for now only

The Machine Learning Engineer is necessary as long as Machine Learning understanding is rare and has a high barrier to entry. As we know, the role of Machine Learning Engineer will be taken over entirely by the common software engineer. It will transition to a standard engineering role where the engineer will get a spec or reference implementation from someone upstream, turn it into production code, and ship and scale applications. For now, many of many Machine Learning roles exist in this weird space where we're attacking problems with ML that just haven't been attacked before. Not long from now, most enterprises will have little need for research efforts to get their projects to the finish line. Only niche use cases and deep technical efforts will require a special skill set. So, pursuing your passion in this field is quite risky.

Need to stay updated

As mentioned earlier, machine learning is a rapidly evolving field. Due to this, machine learning engineers are required to spend a considerable amount of time learning about the latest updates in the field. Reading and learning research papers from various universities and organizations will have to become a regular part of your life if you want to pursue this field. So, unless the idea of continuous learning does not appeal to you, you should rethink your decision of being a machine learning engineer.

Demanding Job

Training models, handling data as well as making and testing prototypes on a daily basis can lead to mental exhaustion. As a machine learning engineer, data munging will also be a painful part of your job. Data munging simply means converting raw, unprocessed data into a more appropriate, usable form. Sometimes you might even have to scrape data from a paginated website and integrate it with your client's internal data while simultaneously dealing with date-time and data type errors. Doing this is no walk in the park and it could get frustrating for some.

Machine Learning seems hard to have a mentor

Most internet influencers preach: Starting with Machine Learning is really easy. You just download the Titanic dataset, copy 10 lines of Python code from a tutorial and you've started with Machine Learning. Here you can find it easy but as the levels get deeper it gets hard. Having a great mentor is really important so that you don't need to figure out everything on your own. Getting a good internship is also a great way to grow as an engineer. It is pretty hard to find a good mentor but with research, we can get it.

Hard to get a Machine Learning job

It is harder to find a job as a Machine Learning Engineer than as a Frontend (Backend or Mobile) Engineer. Smaller startups usually don't have the resources to afford an ML Engineer. They also don't have the data yet, because they are just starting. Do you know what they need? Frontend, Backend, and Mobile Engineers to get their business up and running.

Higher wages

Senior Machine Learning engineers don't earn more than other Senior engineers. There are some Machine Learning superstars in the US, but they were in the right place at the right time — with their mindset. I'm sure there are Software Engineers in the US who have even higher wages.

Machine Learning is future proof

While Machine Learning is here to stay, I can say the same for frontend, backend and mobile development. If you work as a front-end developer and you're satisfied with your work, just stick with it. If you need to make a website with a Machine Learning model, partner with someone that already has the knowledge.

Machine Learning is Fun. Really?

While Machine Learning is fun. It's not always fun. Many think they'll be working on Artificial General Intelligence or Self-driving cars. But more likely they will be composing the training sets and working on infrastructure. The truth is that ML engineers spend most of the time working on "how to properly extract the training set that will resemble real-world problem distribution". Once you have that, you can in most cases train a classical Machine Learning model and it will work well enough.

Conclusion

The aim of this article was to give a critical view that you usually don't hear from influencers. There is no intention to discourage you. If you feel Machine Learning is for you, just go for it. But Machine Learning is not for everyone and everyone doesn't need to know it. If you are a successful Software Engineer and you're enjoying your work, just stick with it. Some basic Machine Learning tutorials won't help you progress in your career.