How Scouting an AI Engineer Should Change Your Hiring Strategy
You’re exploring hiring an AI engineer and let me guess: you’ve turned your request to HR or a recruitment agency who wrote a job description full of markers like “number of year of education” and lowballed the salary. Now you’re getting crickets or worse, a flood of candidates that aren’t what you need. Hiring an AI engineer will require a shift in your thinking. Old job descriptions based on industrial revolution era jobs aren’t going to cut it. If you’re going to compete with Google or Facebook, you’ll have to revamp your hiring process to stay competitive. Here’s how hiring for AI is going to change your strategy.
[Related Article: What is an AI Engineer?]
Results Not Experience
Education and number of years of experience used to be top on the list to weed out unqualified candidates, but working in AI is starting to upend what we know and believe about those qualities. Sure many AI engineers possess PhDs from top universities because those tend to be on the cutting edge of AI research, but not all of your best talent will be formally educated to that extent. It’s possible for talent to rise up in ranks based on non-traditional education methods, so don’t rule out your primarily self taught wonders. Education is part of the business world’s overall worship of experience. AI is such a new field, however, that your candidates just aren’t going to have ten years of experience. It’s not always possible. Instead of these outward markers, focus on what your talent has actually done with their knowledge. Did they spend a year as an intern for a company and transform their customer service? Have they built compelling projects as documented on their Github profile? Experience isn’t going to help if your newest hire is too timid or too uncreative to innovate.
Talent Is Expensive
Competing with giants in the industry for the best talent isn’t going to come cheap. You’ll have to reconsider what your organization begins for starting salaries. AI Engineers are well on the higher end of the salary spectrum, but this doesn’t necessarily mean you’ll have to shell that out. You will, however, have to budget for other perks and incentives that might entice talent away from enterprises like Google. Once you calculate your ROI for having that position in place, you could be looking at the funds to build a salary package like that. If not, there are other ways to attract talent. You could ensure they’ll never have to perform maintenance for legacy code, for example, or allow them significant leeway for creating their projects. Intellectual stimulation and challenge is a top sited work desire, so making sure you leave space for that to happen could be key. You may have to hire secondary roles or accept remote positions, but this could be a small expense compared to the return. You may also have to let go of your laundry list of arbitrary requirements. Knowledge of programming languages is a must, but demanding mastery of one particular language could limit your scope. Unless it’s an integral part of your business (and remember what we said about legacy codes), let your AI engineer operate in the languages and frameworks they know best.
Build Your Own Brand
Your recruitment strategy needs to start within your own organization first. Your business brand should be attracting talent because they believe in the mission. If you have no idea how AI will impact your business and to what purpose you’re implementing these new strategies, your talent may walk. While some may be intrigued by building from the ground up, there must be a clear mission as a starting point. Just like consumers, job seekers want to see that their values align with their workplace. That mission is a selling point for your talent. Consider how fast Google had to shut down Project Maven when workers balked at the idea of working for military operations. In the same fashion, if a potential hire is 100% on board with what you’re trying to build, that could be the deciding factor. Mission statements aren’t some vague platitudes about making the world a better place either. They need to be actionable and clear, pertaining directly to what your organization is doing and what AI will do to further that goal. It may also help to have a separate data and AI mission statement as well.
[Related Article: What are Some of the Best Practices for Hiring Data Scientists?]
Hiring Talent in AI
Right now, it’s a job seeker’s market in the world of AI, so changing your hiring strategy isn’t a suggestion. It’s a must. Unless you’re a multi-billion dollar company with “Gates” attached to the board of directors, you’ll need to figure out how to scout for relevant talent and how to offer what that talent can’t get at a larger organization. At the very least, eradicate those boring hiring benchmarks and job descriptions and start from there.