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Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

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Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#21
post #16
post #13

Earlier quoted context omitted.

Yeah it tells you nothing of value. It's the Recency Bias taken to its ultimate conclusion isn't it. Anything I learned about AI I learned in the last few months but somehow that's as important as all the experience of my decades long software career? Come on.

It signals you don't have enough time or will to chase the latest stuff, otherwise you would have learned it with other cool kids half a year earlier. It carries some signal, but I would not choose based on this alone. I would even consider selecting against this to a certain point, but I also work in a place that COBOL on a mainframe. YMMV.

> I would even consider selecting against this to a certain point

I agree. A technology professional who views themselves as "one of the cool kids" (read: easily manipulated by social media) is a legitimate security threat, as are many of the popularly promoted approaches to "LLM-assisted software developement".

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#24
post #9

I would do the opposite and would likely use the same framework that some AI companies are doing to evaluate candidates. By either not allowing them to use AI assistants in any part of the interview process (Anthropic and OpenAI does this.) or give candidates a strict token limit (100k tokens) until the candidate runs out of tokens. They have to earn it, as the tokens are not free. Given that deskilling and over-reli…

I would start looking for a new job if I heard that my company hired someone who wasn't able to prove that they could write code without LLM assistance. A lot of people like that are legitimately illiterate.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#25
post #19
post #6

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 (human…

Hmmm... I had a different experience. They had a fully automated environment where you ha to write test that passes some tests. No human involved. And the time requirement was insanely tight

WORK FASTER, SLAVE! FASTER!

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#26
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?

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#27
I'm very skeptical of that graph. What does it mean for code to be AI-generated vs human-generated?

If I merge a PR that was opened by Claude do all the changes in it automatically count as AI-generated? How about the individual commits? If I'm reviewing locally and make manual changes but then have Claude create the commit it appears to be AI-generated but might not have been.

The point is that these metrics are easy to game and I've definitely wasted time and tokens refining code with Claude that would have been easier to just edit by hand. It can be kind of fun and when the goal is just "use AI" I don't find it surprising that graphs showing 100% switch to AI-generated code could be defensibly generated without really saying anything about how much manual intervention is happening or how efficient the process is.

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#29

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…

You hire juniors but don’t let them use AI tools for a year :)

Re: Interviewing Engineers in the AI Era: Lessons from a Year of Rebuilding

#30

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…

> 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?

Shouldn't this be a pretty fundamental part of a degree? If you're in school right now for a CS or SWE related degree, I would imagine you're learning how code actually works, how the math actually works, etc.

If an LLM makes a mistake, you should be able to call that out.

You might be arguing that a junior engineer may not have the RWE to make those judgements, but by the time you've completed a 4-5 year degree, you should have done the following:

- Completed coursework where you've learned the fundamentals of programming and software engineering

- Have done dozens of projects (building everything from basic web apps to more advanced pieces of software) where you've seen what works, what doesn't work, etc. This also gives you real world exposure to the latest and greatest frameworks, tools, etc.

- Have done several (at least 2) internships where you've worked at a real company writing real code, and have seen/been mentored into what AI is good at, where it fails, etc.

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