Google DeepMind Research Engineer interview experience

First came a technical screen phase that combined two coding interviews with a separate ML fundamentals interview. The coding parts were LeetCode-style mediums, and the ML fundamentals conversation covered the usual core concepts—optimization and regularization, loss functions, and transformer-related topics—plus practical questions about training and inference.

I didn’t make it past the second stage because I underperformed on one of the coding interviews. The rest of the set wasn’t described as unusually extreme, but the bar was clearly high and the overall outcome ended up being a