Why Retaining Machine Learning Engineers Should Be a Priority

Good engineers are hard to find, and the specificity of a machine learning engineer’s job means that finding a good machine learning engineer is even more difficult. Once your organization finally finds the right talent it needs to progress your machine learning programs, keeping that talent on board is of the utmost importance. However, turnover rates of machine learning engineers have been reported at roughly 10%. This number may seem small at first, but it grows as you consider how many factors are affected by a valued employee leaving your company; the team loses morale, and productivity goes down. The cost can be quite staggering.

This Ripple Effect is Not a Good One

When most companies attempt to quantify the actual cost of losing a machine learning engineer, the numbers typically begin in the tens of thousands, before jumping to a number that rises as high as twice the engineer’s annual salary. With a bottom that is so high, it is no wonder that the monetary cost of losing a talented engineer is a concern for today’s top technology development companies. One reason for this high cost is the expense required to train a new engineer. When one engineer leaves, another takes their place, and the new engineer will need time – and by extension, money – to reach the skill level of the person they replaced.

The Bucks Don’t Stop There

While the aforementioned training costs are disheartening, they are not the only costs that you need to worry about when it comes to finding new machine learning engineers. In order to search for new employees, you and your company must put out advertisements, compose job listings, and begin a general recruitment effort that will use up additional time and resources.

There is also the cascading effect of a known and valued employee leaving your team. Other members of your team will wonder why that person left, and morale could decrease, all while increasing the chances of more engineers jumping ship for a new horizon. The best solution to the problem of potential talent loss is to prevent problems before engineers are hired in the first place. This means every aspect of hiring and onboarding must be reviewed to ensure your company’s processes make and keep machine learning engineers happy.

A Good Feeling from Start to Finish

When the talent is happy, everyone around the talent – and even those indirectly affected by them – will be happy as well. While in the process of recruiting new machine learning engineers, your company should be sure to honestly and accurately portray the overall culture of your team so potential disconnections and missed fits may be prevented before they happen. While introducing new engineers to your teams and processes, the utmost care and respect must be taken to ensure both the new employees and experienced employees are happy. The future happiness of new and current employees depends on this positive integration.

Visit www.PROPRIUS.com for more information on how to improve your team and career. PROPRIUS is an Artificial Intelligence Industry recruiting firm dedicated to projecting organizations to the next level.

If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.

If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.

Joshua Crawford | Managing Director | PROPRIUS

PROPRIUS Focusing on all things Engineering in the Artificial Intelligence Industry. We deliver performance.


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