In the future years, the demand for professionals in artificial intelligence, machine learning, and deep learning will only increase. Here is how to ensure that you obtain the absolute finest.
If you’re an AI-focused tech business, you’ll need to employ a team of technical professionals to create your product. Many need to recruit top-tier AI, machine learning, and deep learning professionals. The demand for these technological specialists is soaring. In 2019, the United States reported a growth rate of 344% for machine learning engineers. In the next ten years, computer and information technology employees are expected to increase by 22 percent, far above the national average.
Despite the profession’s exponential expansion, recruiting tech expertise is challenging. We have often witnessed entrepreneurs make multiple recruiting errors, resulting in delayed initiatives, slowed growth, and lower earnings. These are the three most common mistakes we see businesses make when recruiting AI, ML, and deep learning professionals, and we advise on preventing them.
1. Not Globally Sourcing Talent
There is a talent scarcity for engineers with experience in machine learning despite the increasing demand. In the United States, particularly in tech hubs such as the San Francisco Bay Area, large tech businesses such as Google and Microsoft tend to employ the most available local talent, making it harder for smaller startups to hire as competitively.
To compete, entrepreneurs must alter their perspective: What if we viewed recruitment worldwide rather than locally?
Today’s remote work environment encourages the consideration of internationally supplied personnel. Covid-19 reassigned numerous workers of a technology business to permanent work-from-home positions. Not only is remote work more productive, but enabling it gives your organization access to a layer of global talent that it would not have otherwise been able to attract.
Moreover, deep learning engineers may have access to different employment prospects in specific international locales than they have in the United States, despite their technological expertise. These candidates may leap at the chance to work at a company with an intriguing perspective or challenge to address, adding significant value to your team.
2. Hiring Only Based on Credentials
Before a recruiter views a résumé, many organizations automatically exclude prospective applications. Applicants are denied based on higher education requirements, university name, years of experience, and other factors. It is, therefore, not surprising that fifty percent of candidates lie on their applications.
A Stanford Ph.D. is only sometimes the most accurate indicator of future achievement in AI and ML. In actuality, it is frequently not the case.
Why then? Doctoral candidates are educated to conduct research, publish their results, and repeat. There needs to be more application of technology to real-world problems. In the world of startups, you only require your workers to undertake some of the research in-house. It would help if you had someone who could read scholarly papers, comprehend the principles, draw pertinent insights, and apply them to their current project. If you employ a candidate without technical application abilities, you may rapidly come to regret your decision.
• Preference for teamwork over solitary labor. Creating a product is far more collaborative than people would believe. Ensure that your prospective applicants are team players.
• Appetit for ongoing education. You will need someone to keep up with the newest studies and trends constantly shifting. A candidate that is unwilling to adapt to new techniques and is set in their ways or their comfort zone is not someone you want on your team.
3. Not Testing Programming Skills
You have extended your recruitment efforts abroad and are assessing applicants for experience with applicable skills. What follows? Assessing these abilities
While most AI, ML, and deep learning engineers should have the necessary theoretical understanding, not all are also proficient programmers. If you want to launch a competitive product to market fast, you need engineers who are also professional programmers.
You wouldn’t recruit a new copywriter without evaluating their writing abilities. ML engineer applicants should also be required to adhere to the same philosophy. Typically, firms doing ML, deep learning, or AI talent interviews focus on theoretical topics and never assess candidates’ coding skills.
The exams can be straightforward. You may provide a candidate with a short research paper and ask them to create the stated neural network using an open-source machine learning framework such as PyTorch or TensorFlow. This is an excellent technique to (A) assess their operating speed and (B) their ability to apply study findings to real-world scenarios.
Better Hires, Better Products
Investing quality time and attention into the recruiting process will result in a more marketable and competitive product. This will help you develop a foundation for long-term success in the competitive startup industry by ensuring that you have a robust technical staff that can comprehend cutting-edge research and implements new concepts.
If you are also looking forward to hire Machine Learning Engineers (ML Engineers) then we are here to help you out and provide you custom solutions to all your requirements.