> Finally, an obvious point: evaluation of the performance of an individual software developer is one of the great unsolved problems of software engineering.

I don't think it's obvious at all. That an experienced engineer or manager can correctly evaluate a software engineer is one of the ground assumptions that runs through our industry and its thinkpieces. (Though it is borne out by the studies that, e.g., even Google has done).

> If you think that's different at Google, re-evaluate: Google also runs one of the most famously capricious hiring programs in the industry. Despite constant rituals and genuflection towards data-driven decision making, Google continues to thrive based on its status as a premiere destination for new software developers, despite running a hiring process renowned for the quality of the people it has alienated. There is ample evidence of Google having scaled broken processes.

This is a significant point: Google's hiring process is legendarily bad. While I would work for them (I think their ethics are solid, their engineering is excellent, their benefits suitable for adults, and reports of internal culture are generally good - a rare combination), the hoop jumping they ask you to go through is... quite amazing. I just don't have enough of a taste for doing an independent study of my upper division algorithms class to have aggressively tried to get hired there.

It's also evidence, I think, that broken hiring processes do not inherently break a company - or at least a sufficiently profitable one.

> It's also evidence, I think, that broken hiring processes do not inherently break a company - or at least a sufficiently profitable one.

Also, it's possible that the hiring processes were broken in her chain, but not everywhere. Her experience doesn't seem to align with what either Google or Damore say (he felt all his female coworkers were equally capable and deserved to be there).

> Google's hiring process is legendarily bad ... the hoop jumping they ask you to go through is... quite amazing

How does having a high standard implicate any other hiring practices? "Bad" in one sense doesn't mean "bad" everywhere, and certainly "bad" for you doesn't mean "bad" for everyone. All that said -- I personally don't wish to go through an interview in which I'm grilled in detail on all the algorithms I reviewed in college. Hence why I don't work at Google.

Let me just leave this here:

"We [UrbanAMA] try hard, but again find ourselves with a 98% male candidate pool. You should know that we are an early stage startup that cannot afford market salaries. Despite that, we paid premium salaries to bring a few women who did well in our interviews. But, they lacked the energy to put us into overdrive. Worse, they were starting to drain the energy from the rest of the team. Eventually, we had to do the right thing for the company and let them go. I’m now back to being the only woman on the (tech) team."

I'm not sure what it says about the situation that 98% of the candidate pool is male or that she had to fire specifically the women for draining energy.

She clarified in a comment that she fired men as well as women for "lacking the energy to put us into overdrive." She says:

"Yes, we invest in mentoring and training all our team members and we did with the women we hired as well. We definitely found a few men and women who were either unwilling or unable to take on the role. We did end up losing the men who didn’t as well. On that part, it wasn’t a gender thing really — sorry that the post made it sound like that. Basically we had fewer women to begin with. And as luck would have it, these women did not end up having the desire to do what it takes."

https://medium.com/@hellovidya/yes-we-invest-in-mentoring-an...

I think that her "infinite loop" theory was her perception of the anecdote around hiring at her own startup. It does seem conceivable, but yeah obviously data is light here.

Good point on how the whole interview process is a crapshoot anyway. Hadn't really thought about that aspect, but obviously is a huge opportunity for subliminal bias since it is how it is.

Another weird attribute of her theory about setting the bar and paying premiums for talent is that her engineering team, according to LinkedIn, is in India --- which is a radically different market for software talent than SFBA. In particular, the gender distribution of CS grads and programmers in India is very different from what it is here.

One possible interpretation --- and there are probably equally credible others --- is that this founder found it difficult to compete in the (overheated) SFBA market for talent, irrespective of gender.

That makes it even more weird. The percentage of women programmers there is higher.

https://m.cacm.acm.org/magazines/2015/5/186026-decoding-femi...

Oh interesting. Where did you find this? (My cursory check didn't show turn up any info on that)

I used the following advanced sleuthing techniques (don't share outside HN):

1. I went to LinkedIn

2. I searched for "Silverlabs"

3. I found the page for their company

4. I clicked on the link labeled "14 employees and former employees have LinkedIn profiles"

5. I observed that all the engineering profiles were in Hyderabad.

I'm being a little snarky but also it's good to know how superficial this "research" was so it's not at all unlikely that I'm totally wrong about this.

Even in her anecdote she doesn't actually come out and say they lowered their standards until they ended up hiring women who "couldn't do the work", at least in terms of technical ability. Her only description of these women is:

"But, they lacked the energy to put us into overdrive. Worse, they were starting to drain the energy from the rest of the team."

What exactly does it mean to "lack energy"? Were these women unqualified or otherwise incapable of doing the work? If so, why not say so explicitly? Because otherwise, in the context of startups I would be inclined to interpret that sentence as meaning "they weren't willing to work 80 hour weeks like the rest of our team so we fired them".

Yeah, these are the kinds of vague, subjective terms that are sometimes used to target people whose actual performance is unassailable.

Besides that, a manager/leader/founder needs to take ownership of their team's motivation (within reasonable limits).

>Subjective performance evaluations are easily tainted by prejudice

This is vastly under-acknowledged. Implicit biases affect even the most well-intentioned of us, which then persistently and negatively impact the targets of our biases (see the movie Get Out for an exaggerated, but entertaining take on this).

For an "out-group" in any particular environ, their very status as a historical out-group itself fuels the perceptions that perpetuate their disenfranchisement.

So, the remedy to this has not been to psychoanalyze every hiring manager in an attempt to navigate these murky, subjective waters; but to set objective goals that seek to counter the equally objective under-representation manifestations that we can actually measure.