Coinbase Rebuilds Engineering Interviews for the AI Era
Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding
Coinbase's engineering interviews now test how candidates direct AI, evaluate its output, and apply judgment, since nearly all new code is AI-generated and human-reviewed. After a year of rebuilding, they've rolled out AI fluency signals across the loop, with repo-based coding and debugging as a live signal, and system design with AI in early testing. The process is a living system, updated quarterly, with a working group model and no added rounds.
When the cost of building goes to zero, the cost of identifying what to build, verifying it's correct, and getting it out safely becomes the limiting factor.
- sarmadgulzar
This all sounds good on paper but I have an honest question: How do you develop judgement as a junior or mid level engineer to critique AI if you yourself never learned that skill by making mistakes in the first place?
I’m not saying that all code should be typed by hand in 2026 but there are certain subtle things you learn only when you get into nitty gritty details especially related to security.
Also today’s AI is notoriously bad at ownership. When you ask it to give you a concrete answer, it will still give you options with pros & cons of each so that ultimately you own the decision and not it. So how do you decide between the two (or more) when you never learned to do it yourself?
- dgellow
Something I appreciated when going through Anthropic interview process was that I only dealt with humans. They could have been showcasing how even their interviews are done with Claude or something, but no, the whole loop was interacting with real humans, and nothing was on the topic “how do you work with LLMs”. It was about how me, the human, think and approach situations, and how I handle working with others (humans), etc. Which made a lot of sense to me, and I overall appreciated their process (other than it’s very time consuming, requires lots of prep). But it’s interesting to see that AI labs customers feel the need to showcase how much of their processes are now around agentic stuff
- nitwit005
My company has tried to produce an AI oriented process, and I just don't see it making sense.
The basic problem is, no one on our side has actually doubted the ability of candidates to us the AI tools in conventional ways.
Consider the design section here. I think we are all confident the candidates can query some AI tool for a design. It's basically copy-pasting the description in. The real goal is to have a discussion, to see if they understand the relevant issues. But then, we could just hand them a design.
It just seems like you waste time having them fiddle with prompts and everyone trying to read the output. Just skip to the discussion.
- pards
> When the cost of building goes to zero
Where are they getting all those free tokens from?!? I don't like this rhetoric. It still costs money to write code, only now that wealth gets transferred to Anthropic instead of to individual contributors.
- mchinen
I've been considering different ways to measure code understanding and fluency. I'm still using traditional 'easy' problems on a coding pad to probe this, but I'm finding a new generation candidates that don't prepare for this, and may be able to have a full career with just a higher level of code understanding. But they might not ever develop the ride-a-bike with one hand level of fluency unless they grind pointless leetcodes.
So I'm increasingly uncertain about what and how to test. My default for now is still to rely on ability to write basic code fluently, but I'm open to changing this perspective.
I really want to know how this existing repo AI-assisted live coding test works, with example problems.
It seems the standard data structure puzzle type thing won't be feasible if you are using an LLM.
Also the latency for these agentic coding/prompts seems like it would make the interview a bit awkward.
Anyone been conducting or taking interviews with this kind of thing with thoughts to share?