Engineering jobs, candidates and discoverability
In this and the next few articles, I’m going to try to turn down the heat and shine a little light on what must be one of the most vexing rituals for high-tech employers and candidates alike — the recruitment process for talented engineers.
The professional and financial rewards of success in high-tech have always been attractive, but in today’s world of hypergrowth AI, we’re seeing the ratchet turn even further. We have a perfect storm of provably successful new technology, too few great candidates and spiralling compensation packages. This is attracting new cohorts of engineering talent that might, in other circumstances, have chosen alternative careers. It leads to a more diverse industry and improved candidate pool liquidity, but inevitably, it is a race in which there are some winners and some losers.
It’s self-evident nowadays that if you put the right engineer with the right attributes into the right job, then you get much more out than you put in — it’s a vastly better situation than zero sum. But, as the saying goes, there’s many a slip ‘twixt the cup and the lip. Just making an engineer and an employer aware of each other’s existence in the first place involves an often ad-hoc and imperfect discovery process. Even Google has concerns about discoverability, recently offering AI-powered services (not yet available in all geographies) to help people find jobs. And loosely, discoverability — of available jobs and the people available to fill them — is the theme of today’s article.
The union of all our social networks contains the best potential candidate pool that’s ever existed. With some exceptions, just about everyone has some kind of public social media presence. The trouble is, there’s often a gulf between what employers think they need and how potential candidates describe themselves on social media. Sure, it would be much easier if everyone’s social presence was tagged with an honest, up-to-date resume, but for all sorts of reasons — mainly due to people rather than technology — this isn’t going to happen soon.
This matching problem is tricky, but there’s been encouraging progress on automating parts of it, and some organizations are using AI to augment traditional methods. For example, at leap.ai, where they map out a range of candidate data to assemble a complete picture of peoples career aspirations — including giving them the opportunity to disclose their ‘dream employer’ and ideal role by nominating two companies they aspire to work for. In a recent Techcrunch interview the two ex-Google founders — Richard Liu and Yunkai Zhou — explained how they believe they can guarantee an interview with at least one of their nominated organizations, particularly when they are startups and not industry giants.
If you’re an active jobseeker, then networking/personal and academic contacts, social media and individual recruitment sites pretty much makes you discoverable by a large slice of the mainstream market, although their relative effectiveness is quite volatile. If you’re talented enough to have a shot at elite companies like Google et al, or even some discerning startups, you might decide to target them with a more proactive strategy, working your way out from the core of your social network using your target companies’ employees as waypoints. Remember though, that if you want to work there, so will many others, which partially explains the high number of false negatives that some of these organizations are willing to tolerate. Competing at such ultra-desirable organizations comes at the obvious cost of frustration and disappointment for the candidates who don’t make the grade on the day.
If you ever try to publish a book through the conventional route of engaging with an old-fashioned publisher, they will encourage you to think of creating a platform, by which they mean a halo of other activities and artifacts designed to set your book — and your personal brand — apart from the rest. Framed like this, it’s clear that self-promotion is not limited to publishing — many prominent engineers are avid bloggers, top Stack Overflow performers, open source contributors and so on. It’s not for everyone, but if it’s good, having a similar personal halo is going to increase your discoverability significantly. The folks at Stack Overflow know this as well — their developer story tries to help people showcase the broader aspects of their identities as developers. It by no means represents the death knell of the resume, but this and similar efforts are certainly something to watch, and they also intersect somewhat with our mission here at sourcerer. Social proof and discoverability have been intertwined for a long time and are now probably inseparable.
If you’re an otherwise smart person but still within two standard deviations, I’d forgive you for getting disheartened when you realize you’re competing with candidates like Xiohan Zeng, who, in 4 days in July 2017, interviewed at LinkedIn, Salesforce Einstein, Google, Airbnb and Facebook — and received five offers! One of the key takeaways from Xiohan’s experience was — like it or not — that being able to get close and intimate with algorithms in interview situations can set you apart from the rest in a good way. Xiohan built part of his referral network by replying to the various organizations’ HR posts on LinkedIn — he developed a relationship with their HR people and they, in turn, coached him into their recruitment funnels.
But no doubt, as soon as Xiohan gets someone’s attention, you can be sure he follows up quickly with a great resume.
I spent some time researching what kind of a resume excites hiring managers at some prominent Silicon Valley tech companies — and this included interviewing our own folks at sourcerer. I specifically asked them to imagine what the ingredients were for a perfect profile resume. Resumes, and how talented engineers can use them to stand out from the crowd, are the subject of the next article in this short series.
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