My favorite definition of culture is as a set of rules and norms that communicate what is safe, what gets rewarded, and what gets punished.
Want to know what it’s really like to work at a company?
Forget the fancy corporate pillars and principles and values — if you want to know what truly matters to a place, just look at how it hires, how it fires, and how it promotes talent.
Because who gets hired, fired and promoted is what employees actually see. Those decisions signal loudly to them: this is what is safe, this is what’s not. This is what will be rewarded. This is what is actually valued here.
Hiring process as culture revealer
As a recruiting leader, I hired hundreds of people at Google, then at Uber, and again at Netflix.
The companies all “said” so much through their hiring process. In part, this was strategic and by design. But they also couldn’t help it -- it was the way they ran!
So what can a company’s hiring process tell us about its culture? What are the signals we can process from the outside, to know what it’s going to be like on the inside? How do we reduce the risk of going to a company we’re not aligned with and can’t thrive at?
Let’s take a look!
Google: Work = School + Tons of Data
Working for Google felt a lot like college. The concentration of PhD colleagues, the cafeterias and laundry rooms, the guest speakers and lectures, all gave it an intensely collegiate vibe. For years, I took a bus to the “campus”, played soccer there after work, then ate dinner with friend-coworkers in the campus cafes.
Google was also the most data-obsessed organization I’ve worked for. It’s belief in data, and in the need to test, learn and make decisions from data was supreme. And it applied as much to people decisions as it did to product and engineering ones.
The academic feel and engineering-driven approach made sense: Larry and Sergey left a PhD program in computer science at Stanford to found Google, and their parents were academics and scientists.
How did this data-driven, academic culture then show up in Google’s hiring process?
For one, we required GPA’s and transcripts from everyone who applied. We then submitted that information to multiple “hiring committees'', modeled on university hiring committees, for those groups to review. Employees on these committees never interviewed or met candidates. But they decided whether or not to hire a candidate.
By contrast, the hiring manager with the hole on their team? They met the candidate, but had zero authority over the hiring decision.
And that was precisely the point: a hiring manager who was staying up late at night because of the need to hire for their team was simply not objective. The hiring committee? They were not so desperate to hire. And because they never met the candidates whose fates they decided, committee members could make more objective hiring decisions.
To make the hiring call, committees had access to GPA’s, to transcripts and to interview scores (which were down to the decimal, on a 1.0 - 4.0 scale). All of this was presented to them in the form of a hiring summary or “packet”.
If there was something more open-ended to summarize (for instance, a candidate’s leadership history) that also had to be presented objectively. Everything in the packet had to be removed, factual, dispassionate. If it wasn’t, a committee might perceive the packet as salesy, landing an otherwise-qualified candidate in the reject pile.
This all reflected Google’s algorithmic, engineering roots. And the emphasis on data reduced the subjectivity and biases humans have when making decisions. Interview decisions about other humans being especially vulnerable to bias.
Candidates applying to the company would speak to a Google recruiter for weeks on end about their GPA and transcripts, and about “going to the hiring committee”. So they definitely got a sense of the academic fabric that awaited.
The interview experience was also process-heavy, and long. For years, candidates went through upwards of 10 or more interviews, and multiple rounds of submission and resubmission to various hiring committees. The process often took months.
So once you got into Google, you were wowed by the intellects, and the perks; by the scale and ambition of the place. But removed, data-driven decision-making that took time to make: not so surprising.
Uber: get it done
Uber’s culture was less engineering-dominated than Google’s. It also didn't have a single sales employee in the years I was there (2013-2016). So what really made Uber, “Uber”, was its culture of operational excellence.
Speed, execution and the willingness to do what was needed were what counted. The culture reflected the scrappiness of Uber’s founder, Travis Kalanick. Kalanick had studied computer science, but brought a grounded earthiness to a business that was intensely physical: at the end of the day, we were moving people and cars around a city.
What did that mean for someone who applied to the company?
For one, we never asked about a person’s schooling, much less their GPA.
But we cared so much about execution that we asked to see people’s work. So we gave hiring “exercises” to candidates during the interview process. Thousands of Uber’s first employees were asked to take some form of creative, analytical, or commercial exercise.
The exercises were purposely vague, the prompts brief, and candidates could put their output into a format of their choosing. All of this reflected the open-ended nature of the problems we had to solve, and the ownership culture we were a part of. We wanted to know how people would structure their output and thinking. We wanted to see their work.
Exercises gave three loud, distinct and efficient signals:
- quantity of effort — you could open an exercise and get a quick, reliable sense of how much the candidate had put in. Of whether they phoned it in and did the bare minimum, vs. putting a solid foot forward, vs.putting their best foot forward. It was in the formatting, the intentionality, the visual presentation of the content.
- quality of effort — this required a full read of the exercise. What was the level of detail? Did the candidate take time to compress and distill some of the questions and their answers? Did they do research? Absent more data from us, what was the quality of the assumptions they made?
- Willingness to make the effort – quality and quantity aside, just by returning something on time, there was a signal here: that even with the day job most candidates had, they were serious (or not) about this opportunity.
When I interviewed with Uber in Singapore, an interviewer gave me a referral code to share with my friends. The code gave users a discount on their first few trips. But the point wasn’t to make me look good with my friends. And it wasn’t that I'd ignite regional subscriber growth! The point was they could track how many signups I got: they wanted to know if I’d be willing to put in the effort. They wanted to see the work.
Speed was also valued at Uber and that came through in the interview process: deadlines for the exercises were tight. It was rare to go through more than five interviews. When we extended offers, we didn’t wait around for you to decide: you had seen us up-close, and you were in or you were out. If you didn’t want to come that was fine, but there was no use in drawing things out.
All of this was consistent with an ops-driven culture obsessed with scale and efficiency. The goal was always a mechanized, repeatable process for anything we did more than once. And since we were interviewing hundreds of candidates a month, the hiring process had to be entirely consistent and predictable.
But this repeatability would contrast sharply with the firm I would hire for next, Netflix.
Netflix: the anti-machine
Netflix’s culture was extremely fluid. There were far less rules, in favor of speed, and of empowering employees to have as much ownership and creative impact as possible.
In practice then, Netflix was very process-light. It always existed, but in the early years I spent there, “process” was almost a dirty word because of what it connoted in terms of inefficiency and lack of speed.
The company was also strikingly candid in terms of feedback and performance: it was a culture of constant learning and improvement, and core to being able to improve – as individuals, as teams and as a company – was the ability to give, receive and use feedback.
When it came to hiring, no one at Netflix was more empowered than a people manager. Managers had an extraordinary amount of freedom (and responsibility) to define the high-performing team they needed to build; to hire whoever they felt was best for that team; and to evaluate those hires in whatever way they believed would provide the inputs for their hiring decision.
How did all of this translate into the hiring process?
Whereas Google and Uber were highly mechanized, Netflix was the anti-machine.
It was the anti-hiring committee to Google’s removed, consensus-based approach. At Netflix the buck stopped with a hiring manager: they decided whom to hire, and owned the performance and success of the hire. Full stop.
You want to hire somebody? Go ahead. Be fast about it, be thorough, and be ready to own the upside or downside of your decision. But you’re an adult, you know your need best – so own it.
Netflix was also the anti-playbook to Uber’s repeatability. Every single role we hired for began with: “What do you want the process to be, hiring manager?” This made the process bespoke by role because it was up to the manager to decide the number, sequence and content of their interviews.
What did a candidate experience from their end?
Speed: Netflix managers empowered to make the hiring decision directly meant we were much closer to Uber’s levels of hiring speed, than to Google’s. If a manager felt they had the inputs they needed for the decision, they could decide and extend an offer.
Candor: to a far greater degree than at Google or Uber, the recruiting team at Netflix provided feedback to the candidates we interviewed. During a phone interview, in-between interview rounds, after the process ended, candidates got both positive and critical feedback on how they were interviewing.
We wanted to see how they reacted to the feedback: were they self-aware, reflective and wanting to improve? Better yet, would they take action on that feedback and adjust in the next interview?
The upshot was that it was rare for a candidate to join Netflix without knowing they were opting into a culture that was fast, fluid and candid.
The signals are there -- you just need to process them
Every time you opt into a new job, you take on risk. Risk around the role. Risk related to the boss you’ll work for. Risk of the company’s business going south. Risk around the company’s culture not being what it’s cracked up to be.
Meantime, interviewing is an intense, emotional exercise. It’s stressful and time-constrained. You feel powerless over the speed and outcome of the process. It can be so opaque.
Then, boom -- you get an offer! And as soon as you do, the clock starts ticking on your decision.
So it’s best to pay attention to these signs as you interview. Best you look at factors like speed, feedback, decision-making, repeatability. Listen for those signals, and they’ll paint a picture you can use to reduce risk and opacity, and to make a smarter career choice.