I’ve done the “at home” test for ML recently for a small AI consulting firm. It's a nice approach and got me to the next round, but the way the company evaluate

I’ve done the “at home” test for ML recently for a small AI consulting firm. It's a nice approach and got me to the next round, but the way the company evaluated it was to go through the questions and ask "fundamental ML bingo" questions. I don't think I had a single discussion about the company in the entire interview process. I was told up front "we probably won't get to the third question because it will take time to discuss theory for the first two". If you're a company that does this, please dog food your problems and make sure the interview makes the effort feel valued. It also smells weird if you claim it's representative of a typical engineering discussion. We all know that consultancy is wrangling data, bad data and really bad data. If you're arguing over what optimiser we're choosing I'd say there's better ways to waste your customer's money. On the other hand I like leetcode interviews. They're a nice equalizer and I do think getting good at them improves your coding skill. The point is to not ask ludicrously obscure hard problems that need tricks. I like the screen share + remote IDE. We used Code which was nice and they even had tests integrated so there wasn't the whiteboard pressure to get everything right in your head. You also know instantly if your solution works and it's a nice confidence if you get it first try, plus you can see how candidates would actually debug, etc.