Runway·Machine Learning Engineer·Technical Phone Screen
May 2026
Interviewed for an ML Engineer role at Runway and got hit with a pretty deep n-gram language model question that covered implementation, design decisions, and complexity analysis all in one shot. More of a systems-meets-theory hybrid than a pure coding screen.
- Design and implement an n-gram language model class with fit and generate methods. The fit method should read a text file, tokenize it, build n-gram and (n-1)-gram frequency counts, and compute conditional probabilities with smoothing. The generate method should sample next tokens using the learned probabilities. Also discuss how to choose the optimal n, validation procedures, metrics like perplexity, backoff or interpolation strategies, and the time/space complexity tradeoffs for different values of n.
“This was a lot to unpack in one question.”