Much conventional wisdom around recruiting centers on only hiring “the best” (or the “rockstars” or “A players” or “10x engineers”—terms many engineers dislike!). Under that model, companies should hold a high bar and reject candidates whenever they are in doubt.
In reality, this advice oversimplifies the complexity of hiring well. “The best” is hardly as precise a concept as we’d like to think it is. Technical software roles can be highly specialized in both hard and , and teams vary widely in values, expectations, and style of work. In spite of technology stacks and qualifications being listed succinctly as if they are menu items—“3+ years of Python” and “GraphQL and Node experience a plus”—an engineer with those specific skills might excel at a large enterprise company, but struggle to meet expectations at a small startup in a role that has a seemingly similar . Or they might excel at the startup but find the lower cash pay or the stress of uncertainty incompatible with their life—someone with “the best” experience may not be the best fit for your company.
There are recruiting advantages to specificity around what makes a great engineer. If you define “the best” the same way many other companies do (by traditional pedigrees like a four-year degree from a top school and time at a FAANG company), you’ll end up competing with a large number of companies for a small pool of mostly homogenous candidates. Will you be able to win those candidates over? Can you pay the top-of-market compensation that those candidates expect? Are they actually the candidates most likely to help your company succeed and to succeed in the role? What qualifications are actually essential for success?
Rather than battling the forces of supply and demand, you could rethink what attributes you really value. You might also explore other, creative ways of hiring great candidates that are underappreciated in the market, like candidates that other companies might frequently overlook, or looking in adjacent markets. For example, when Lyft was building its self-driving car team, technical talent with familiarity in that space was in short supply (the self-driving industry is young) and high demand (due to competition from both larger companies and up-and-coming startups in the self-driving space). Rather than just compete for the same candidates as everyone else, Lyft realized that the much more established gaming industry employed many people with similar skills and interests, and tapped into that much larger and less-competitive pool of candidates.