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How To Interview At ML Companies

A few folks have been asking me if such-and-such would be good AI/ML company to work at. If you’re a data scientist or engineer and are considering a job, here are some interesting questions to ask during the interview. Note: these are focused on the business, not the technology.

  1. Why does anyone need this? Like all advice, this sounds deceptively simple. But make sure you get a very compelling answer here. Many AI companies are a solution-in-search-of-a-problem. Reverse engineering from the technology to the market almost never works.
  2. How was this problem being solved before the AI came around? Was the pre-AI “manual” solution good enough? Common answer: “we’re replacing humans.” That isn’t enough. Often having a human is desirable (bedside manner, dexterity, perfection a requirement). Often a human is affordable due to margin structure. You’re looking to get a sense that the product provided is something that was never possible before, 10X better, or just-as-good but 10X cheaper. Not 20% cheaper. 10X.
  3. How many users have you spoken to? What have you learned from them? All founders talk to some users, but few talk to enough users. Too often I meet founders who are convinced people will want their solution based on limited data-points. The best founders are endlessly talking to their customers. Importantly, they have intimate knowledge of the underlying problems users have, as opposed to a collection of anecdotes about the specific solution being offered in the product today. This expertise is important when building stochastic product (“how much recall/precision do we need to launch?”).
  4. How do you make money? Be on the lookout for what I call “multistage rockets”: “Today, we’re doing X. But our grand plan is to do Y, which will be really profitable”. These usually fail.
  5. How will you grow? How will anyone find out about you? Bad answer: word of mouth. Everyone wants to have a positive k-factor. Sometimes it works out (I’m sure you’d love to be early at Facebook). Making a viral product demands striking gold or possessing incredible artistic finesse about what makes humans tick. Unless you’re seeing either one of those, I’d suggest looking for the time-tested alternative: paid marketing. A great answer includes the cost of acquiring a customer, life-time value of a customer, marketing channels used, etc.
  6. How big is this market? I suggest this only as a founder-mentality canary test. Are they focused on making a massive company, or doing research? A bad answer is just saying a really big number. “$400B”. A better approach will have a back-of-the-envelope calculation which once multiplied out paints a picture: “We make $10 per customer per month. We think there are about 150,000,000 people in this market, so that’s $18B of annual revenue.”
  7. What is defensible about the business? Bad answer: an algorithm. In software algorithms are rarely sustainable moats. Google got great because of PageRank, but it stayed great due to network effects.

There many other factors to optimize for, like the people you’ll work with, the technologies you’ll work on, commute, etc. I hope this is a helpful guide at sizing up the market elements of the decision. I’d be happy to help with any personalized advice. My email is daniel@dcgross.com.