3 Mistakes you better avoid when hiring Artificial Intelligence and Machine Learning Engineers

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You can make sure you're always in high demand by becoming an expert in artificial intelligence, machine learning, and deep learning.

You need a team of experts to develop your product if you want to be successful in the AI space. This is your chance to get in on the ground floor of an exciting and growing industry. Overall, computer and information technology jobs are predicted to grow by 22 percent in the next ten years, well above the nation’s average.

Despite the exponential growth in this field, hiring tech talent is not always easy. Startups often make the same hiring mistakes, again and again, leading to problems such as stalled projects, slow growth and decreased profits. Here are three of the biggest mistakes we see startups make when hiring AI, ML and deep learning engineers - and how to avoid them.

THE 3 BIGGEST MISTAKES STARTUPS MAKE WHEN HIRING AI, MACHINE LEARNING AND DEEP LEARNING ENGINEERS

1. Not recruiting globally

Despite the high demand, there is currently a shortage of talent for engineers with machine learning experience.

To be able to get any talent, startups need to hire globally, instead of locally.

The current remote work environment has made globally sourced talent more attractive to companies. Not only is remote work more productive, but it also allows companies to tap into a pool of top global talent.

In addition, in some parts of the world, deep learning engineers may not have the same job opportunities as those in the United States, even though they have more advanced technical skills. These people may be more inclined to work for a startup that has an interesting angle or problem to solve, and could potentially bring substantial value to your team.

2. Only looking at education

University name, education requirements or experience can be things that companies often value highly and therefore filter people before looking at their resume. That is also why people lie on their resumes.

A Ph.D. does not always mean better performance in the fields of AI and ML.

Doctoral students are typically trained to research a problem, publish their findings, and repeat. However, there is often little technical application to real world problems. Instead, you need someone who is able to read and comprehend academic papers, understand the concepts, and be able to derive insightful conclusions that can be applied to the project they are working on.

Also:

Make sure your candidate can work in a team. Creating a product is a very team-based challenge and takes lots of collaboration projects.

The applicants should also be eager to learning new things. You need people to stay on top of things and keep up with current trends and research. In this field, people can't stay put and continue doing what the do for 40 years.

3. Programming Skills

The next step after expanding your recruitment globally and vetting applicants for applied skills experience is to test those skills. Although AI, ML, and deep learning engineers usually have the theoretical knowledge you need, not all of them are good programmers. If you want to ship a competitive product to market quickly, you need engineers who can also program well.

Startups who are interviewing ML, deep learning, or AI talent should focus the interview on actual coding abilities rather than theoretical concepts.

Tests can be simple. For example, you could give a candidate a research paper and ask them to create the neural network described using an open source machine learning platform, such as PyTorch or TensorFlow. This is an excellent way to (A) determine speed and (B) see how research concepts can be used in a real world scenario.

Better Staff, Better Products

The most important thing is that if you invest a lot of thought and time in the recruitment process, you will end up with a product that is more marketable and competitive. By doing this, you will build a strong technical team that can both understand cutting edge research and apply new concepts, helping you establish a foundation for long-term success in the cutthroat startup marketplace