AI/ML interviews can feel uniquely intimidating because the field is broad. In practice, most interviews test a narrower, more predictable set of things than candidates expect.
Know the fundamentals cold
Core concepts — overfitting, evaluation metrics, the basic mechanics of the models you claim to know — need to be second nature, not something you're deriving live under pressure.
Be ready to explain your own projects in depth
Interviewers frequently go deep on a listed project rather than asking abstract theory questions. If it's on your resume, you should be able to explain every design decision in it.
Practice explaining trade-offs, not just definitions
"When would you use X instead of Y" questions are common and test judgment, not memorization. Practicing this framing matters more than memorizing more terms.
Don't neglect the practical/coding component
Many AI roles still include a general coding round. Treat it with the same seriousness as the AI-specific portions — it's often the actual filter, especially for junior roles.