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Artificial intelligence and machine learning is a growing field. The amount of talent in the pipeline is not nearly enough to meet the demand as more and more companies turn to AI solutions. Universities are now developing curricula to meet these needs, but at present, experienced ML researchers and engineers remain highly sought after. So how can your business locate emerging talent in the area? And how can you differentiate yourself enough to convince them to come work for you?
Here are seven ways companies find the AI/ML workers they need, even in a tight hiring market. Even small companies can try these tactics to catch the talent, whether junior or senior level, that could otherwise slip by.
1. Cast a wide net
First, accept that you will need a different strategy for recruiting machine learning junior engineers versus senior researchers. The prevailing strategy these days when looking to hire junior-level engineers is to cast a wide net. According to eBay vice president of engineering Japjit Tulsi, it is necessary to broaden the scope beyond AI-specific backgrounds “because AI roles are important to most companies right now and so the actual true data scientists and applied researchers are few and far between.” Instead, companies should seek resumes that suggest an aptitude for adaptive learning and a commitment to tackling hard challenges.
2. Partner with universities
According to SnapLogic chief scientist Greg Benson, partnering with universities is a powerful way to recruit promising junior engineers. “It’s definitely worthwhile for companies who are looking for relationships to engage with academic departments,” he says. Corporate sponsorship of student projects enables businesses to identify top young talent and lets participating students experience machine learning work firsthand. Impressively, a third of SnapLogic’s engineers are students from its university internship program, though it doesn’t hurt that Benson is also a professor at the University of San Francisco.
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3. Host a hackathon
Hackathons are increasingly used to identify top coding talent and quick-thinking creatives. These events bring together people with technical backgrounds to address a problem and collaboratively code a solution from scratch. Sugi Venkatesh, HR division vice president at ADP, played host to several successful hackathons at Georgia Tech. He explains that “for these niche areas, like AI and ML, we have unconventional hiring constructs.”
4. Look to education programs
As the demand for AI talent grows, a number of education programs now offer specialized courses to train junior talent and help them find jobs. Abhi Jha, director of advanced analytics at McKesson, hires data science students from Galvanize, a technical skills training provider. “We’ve had a lot of success hiring from career fairs that Galvanize organizes, where we present the unique challenges we solve in health care,” he says.
5. Sponsor AI conferences or competitions
Hiring experienced data scientists and machine learning researchers requires a different approach. These people are easy to locate — through network connections, academic papers, and academic conferences — but difficult to recruit due to high demand. Many companies sponsor AI conferences or competitions in order to attract international talent and build their corporate reputation as an AI supporter. These events also expose potential candidates to the attractive features of the host company, such as large, high-quality datasets or interesting problems to solve.
6. Budget for at least one A-level player
Dominant tech companies have a significant advantage when it comes to recruiting the top tier. Google and Facebook hire university professors such as Geoffrey Hinton, Fei-Fei Li, and Yann LeCun with plum appointments and endless resources. This is a valuable recruitment strategy because A-level players want to work with other A-level players. Offering junior candidates the opportunity to work with established experts or offering experts the best and brightest of the new recruits appeals to both parties.
7. Retrain existing teams
Finally, the difficulty in finding experienced talent is inspiring many companies to offer retraining to update the skills of existing engineers. Explains Jenny Dearborn, chief learning officer and senior vice president at SAP, “We are always upgrading the skills and competencies of employees to align with being able to achieve our business objectives.” Larger firms can do this through corporate training programs, while smaller firms might bring in external trainers. Extended education courses, apprenticeships, and mentoring programs are all ways to bring greater machine learning experience to your team.
Closing the deal
At the end of an interview cycle, a strong AI candidate will typically have multiple offers in hand. To differentiate your company, show how a successful candidate will be able to make a meaningful impact and be core to your business’ success. This might be most important in signing millennials, who, according to Venkatesh, tend to be looking for a purpose.
Recruiting talent for AI projects may not be easy, but by tailoring your approach, developing strong partnerships with universities, and employing creative solutions like hackathons or academic conferences, it is achievable. Differentiate your company by offering strong proprietary data sets, top-level colleagues, and interesting and meaningful projects and the best candidates will find you.
Adelyn Zhou is the chief marketing officer at Topbots, a strategy and research firm in applied artificial intelligence and machine learning.
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