The 3 biggest mistakes to avoid when hiring AI and ML engineers

If you're a tech startup in the AI ​​space, you need to hire a team of technical experts to develop your product. For many companies, this includes sourcing top-notch artificial intelligence, machine learning, and deep learning engineers. The demand for these technical experts is rapidly increasing.The United States cited 344% growth rate Overall, machine learning engineer employment in 2019 is predicted to be as follows for computer and information technology jobs. grow by 22 percent Over the next 10 years, it will far exceed the national average.

Despite the field's rapid growth, hiring technology talent is not easy. We've seen startups make hiring mistakes time and time again, leading to stalled projects, slowed growth, and reduced profits.These are the three biggest mistakes startups make when hiring his AI, ML, and deep learning engineers — and how to avoid them.

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1. We don’t source talent from around the world.

Despite high demand, there is currently a shortage of engineers with machine learning experience. In the United States, especially in tech hotspots like the California Bay Area, large tech companies like Google and Microsoft tend to hire most of the locally available talent, giving smaller startups a competitive advantage. It is becoming difficult to hire new employees.

To compete, startups need to change their perspective. What if we thought about hiring globally instead of locally?

In today's remote work environment, globally sourced talent is ripe for consideration. COVID-19 has forced many tech company employees to permanently work from home.Not just remote work more productiveBut allowing it does give your company top-level global talent that it might not otherwise have access to.

Additionally, in some parts of the world, deep learning engineers may not have the employment opportunities they have access to in the United States, despite having advanced technical skills. These prospects jump to work at startups with interesting perspectives and problems to solve, and can bring tremendous value to your team.

2. Hiring based solely on credentials

Many companies now automatically filter out job candidates before recruiters see their resumes. Applicants are rejected based on higher education requirements, university name, years of experience, etc.Because of this, it is no wonder 50% of applicants Lie on your resume.

and a Ph.D. in AI and ML. Stanford's grades aren't always the best predictor of future performance. In fact, this is often not the case.

Why not? PhD students are trained to investigate problems, present their findings, and iterate. It has little technical application to real-world problems. In the startup world, employees don't actually have to do most of the research in-house. Instead, you need someone who can read academic papers, understand the concepts, derive relevant insights, and apply them to the project you're working on. If you hire an applicant without applied technical skills, you may quickly regret that decision.

Also consider:

• Prefer team work to solo work. Building a product is a much more collaborative process than most people think. Make sure potential candidates work well in a team approach.

• Motivation for continuous learning. You need someone who can stay on top of the latest ever-evolving trends and research. Candidates who are stuck in their ways and comfort zone and aren't open to adopting new approaches aren't the kind of people your team wants.

Read more about Built In's expert contributor networkWant to pursue a career in machine learning? Here's what you need to know.

3. Don't test your programming skills

You're expanding your recruiting efforts globally and scrutinizing applicants' applied skills experience. What's next? Test those skills.

Most AI, ML, and deep learning engineers should have the necessary theoretical knowledge, but not all are good programmers. To bring competitive products to market quickly, you need engineers who are also good programmers.

You wouldn't hire a new copywriter without testing their writing skills, right? This same mindset should become the norm for aspiring ML engineers. Startups interviewing ML, deep learning, or AI talent often focus their interviews on theoretical concepts and don't test candidates' actual coding abilities.

Tests don't have to be complicated. For example, you can assign candidates a short research paper and ask them to build an outlined neural network using an open source machine learning platform such as PyTorch or TensorFlow. This is a great way to (A) see how fast you can work and (B) see how you can apply research concepts to real-world scenarios.

Better recruitment, better products

The bottom line is that if you invest quality thought and time into the hiring process, you'll end up with a more marketable and competitive product. This ensures we build a strong technical team that understands cutting-edge research and can apply new concepts, laying the foundation for long-term success in the competitive startup market.