Interview Questions

Good interview questions don’t just test whether someone has seen a concept before. They take a familiar idea (a loss, an optimizer, a data structure, an evaluation metric) and add a small twist that forces the candidate to think: adjust an assumption, modify a formula, or adapt an algorithm. The goal is not to reward memorization, but to see whether the person can reason from first principles and assemble a solution under new constraints.

Bad interview questions are mostly trivia: “What is X?” or “Define Y.” That kind of knowledge can be acquired quickly on the job. What’s harder (and more predictive) is the ability to think critically and creatively, and to combine tools rather than follow a single pattern learned in school. A good question checks whether a candidate has enough tools and judgment to approach an unfamiliar problem — not whether they can name every tool in isolation.

Machine Learning

• Cross-Entropy with Variable Number of Classes

  Suppose you are training a model where each sample has a different number of valid choices, but exactly one of them is correct. How would you design the loss computation in this setting? What assumptions would you need to make about the model outputs, and what pitfalls might arise?