How to win the recruitment war for machine learning talent

The machine learning talent you’re looking for is out there. These in-demand experts simply require a little more creativity when it comes to recruiting.

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As the uses of Artificial Intelligence (AI) technology expand into every industry, companies like yours might be looking to hire experienced AI and, more specifically, machine learning (ML) talent from a small pool of available candidates stateside. But how can you find, entice, and recruit ML talent when you’re competing with major tech giants like Google, Amazon, and Microsoft? The answer lies in getting creative with your recruitment and hiring strategies.

Adjust recruitment strategies based on experience

The first thing to know when reevaluating recruitment strategies for high-end machine learning (ML) or other AI roles is that you'll need to adapt strategies based on the experience level you're looking for. What works for a Jr. ML Engineer won't work for recruiting Sr. AI Researcher. To access the talent you're looking to hire, you need to go where they'll be found.

For more junior-level roles, universities, hackathons, and specialized training programs are great sources of professionals that are versed in the latest tech that can help build out your AI/ML department, and then transition into senior-level roles. For more senior or experienced roles, qualified applicants are most commonly found through network connections, academic papers, and academic conferences. Understanding the need to adapt your recruitment and hiring strategies based on the level of experience you're looking for will set you up for greater success when it comes to attracting and retaining the professionals you need.

Provide opportunities that motivate talent to switch jobs

Just how hard is it to net the machine learning professionals you need? According to recent findings by Paysa, Amazon's average annual investment in AI and machine learning hiring is $227.8 million, with the next major competitor being Google with an annual investment of $130.1 million. But the good news is you don’t have to have a similar budget to net the experts you need. You just need to know what motivates talent, so you can provide the opportunities that entice high-end, in-demand talent to switch jobs.

When it comes to in-demand ML talent, their motivations boil down to the following: intellectually challenging opportunities, competitive compensation and resources, location, brand recognition, diversity of problems, the impact of their work, and the quality of the team. The average salary range for Machine Learning Engineers and ML Scientists is $125,000 to $175,000, according to Mondo’s 2018 Salary Guide. While these salary ranges are on the higher end of the spectrum, you don't have to net the same revenue as Amazon or Google to offer a competitive compensation plan. Instead, you can look at your annual budget for your Tech department and see where you might be able to pull some funds together to offer a competitive rate after considering the ROI this role provides.

If you can't afford to pay a salary in this range, look into long-term incentives you might be able to offer as an alternative. For example, consider incorporating remote work flexibility if you're located in an area that has a hard time recruiting top ML Engineers. Analyzing and providing these incentives will help you recruit machine learning talent that would otherwise be out of reach.

Know how to sell the opportunity

When it comes down to it, qualified ML talent accepts job offers and pursues job opportunities at major Tech leaders like Google or Microsoft because of their brand recognition. Accepting an opportunity at one of these businesses, especially in one of their more experimental departments like AI or machine learning, means they’ll have access to the top tech available, be able to collaborate with leaders in the space, and have a fair amount of autonomy.

Employers like Google don’t have to do the extra work of selling candidates on the role because their brand exposure does it for them. While your business may not be an instantly recognizable brand, learning how to sell the job opportunity can be an extremely effective hiring strategy to net in-demand professionals. Identify what your company provides that competitors might not be able to and communicate this during the interview. If your business routinely hires from within, encourages autonomous work among the Tech team, or invests in various skills development opportunities for employees, let potential candidates know.

Not everyone thrives in environments at large Tech companies. Expressing the differentiators between your business and leaders like Apple in what you’re able to provide, whether that’s with work environments or internal growth opportunities, could help sway talent to accept your offer instead.

Partner with universities

As referenced previously for more junior-level roles, partner with a university and funding or supporting a school project can open up a machine learning talent pipeline that leads to paid internships and post-graduate employment. Given the short supply of in-demand AI-based talent, some companies have found this to be an incredibly effective tactic to recruit machine learning talent directly from the source.

If you decide to go down this route be sure to fully flesh out the project you plan to pitch to the schools, you have in mind. Given the success of this strategy for a variety of companies looking to recruit machine learning talent directly, these types of partnership programs have become more popular. Meaning you'll need to develop an exciting project and clearly articulate the benefits of it to attract interested students.

These are just a few of the recruitment and hiring strategies I’ve seen at work in the marketplace that offer your business the best odds of recruiting in-demand ML talent. However, given the extremely limited pool of high-end, experienced candidates to choose from, another option is to outsource to an external recruitment agency. Ultimately, it comes down to what works best for your business. Experiment with these strategies, compare your results, and adapt when necessary. The machine learning talent you’re looking for is out there; these in-demand experts simply require a little more creativity when it comes to recruiting.

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