Guest
Nirmal
Machine learning interview advice usually sounds like someone handing you a checklist they barely used themselves. Nirmal brings the hiring-room version instead: what the loop feels like, what strong candidates do early, and where people waste months getting ready for the wrong thing.
MicrosoftNirmal BudhathokiMachine Learning InterviewsTechnical ScreensProjectsCode Examples
Why this page is worth your click
If you are staring at ML roles and wondering whether to study theory, grind projects, or fix your storytelling first, start here. Nirmal helps drag that panic into plain English.
What makes his page useful is that he does not sell a fantasy. He talks like someone who knows the market is rough and still believes there is a clean way through it if you stop treating prep like a personality trait.
Come here if you care about
- how strong ML candidates think before they answer
- which prep actually changes outcomes
- how to talk about your work without sounding rehearsed
- what hiring teams notice when everyone says they love AI