The interview began with relatively standard questions: a graph algorithm coding problem and basic machine learning inquiries. However, it concluded with an unexpected twist – my interviewer constructively critiqued my interview skills and offered career advice relevant to Machine Learning Engineers (MLEs).

The interviewer noted that my approach was too slow, too academic, too comprehensive, and too indirect. They emphasized that what was needed was a clear and efficient answer to their questions and coding problems. As someone relatively inexperienced, this observation was absolutely on point and prompted me to consider how to optimize my interviews to appear more professional, rather than just "winging it."

One reason this is important is that time spent on non-essential details when answering problems or questions detracts from time available for asking more questions. These questions help the interviewer gauge your skills and can potentially make you more competitive.

(To be fair, I did purposely take things slowly to encourage clear communication and discussion. However, it's important to know when to be deliberate and when to be concise.)

This was a really unique experience. While I'm not sure if everyone else will have the same outcome, this interview is definitely worth trying. You might learn things here that you wouldn't from other interviews, such as those at Amazon or Google.

Graph Coding Problems + ML Concepts