Being a hiring manager at a big company, I can tell you this is just as frustrating for me as it is for candidates. I hate “leet code” and frankly find algorithmic interviews to be very low signal compared to more practical, open-ended, domain-specific problems.

I will say though, the problem is one of “standardization” across an organization where it’s too big for everyone to fit in a room.

Suppose you give each team high autonomy to hire whoever they like using whatever “good” process they come up with. 90% of the time this results in good hires. But as you grow, that ten percent of underperforming people becomes large in absolute numbers, and is very painful to deal with.

It becomes a real problem when relatively lower performing people end up concentrated on a team, and then start being the hiring gatekeepers for that team, thus multiplying the number of lower performing hires.

Later you start having institutional problems when everyone starts to perceive that the engineers in Department A are generally better than the engineers in Department B. Engineers in Department A are more likely to leave if they perceive the company is getting worse at engineering - it becomes a self-fulfilling prophecy.

Then you get enormous pressure to come up with standardized testing - aka algorithms on the whiteboard, or some other academic inspired exercise - imposed by higher level leadership that wants to address a genuine problem (skill disparity across the org) but does not know any better way to do it.

I think, as PG points out, there may be a real opportunity to innovate here, and probably a big financial opportunity if anyone can figure out how to productize and scale a solution.

I struggle to see an easy answer, though. In a utopian universe (for a hiring manager) I’d do something like pay candidates to come on site and work for a week, then make a hire/no-hire decision based on that. But I think that is far too onerous for candidates (and a big company) to have legs.