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Interview Prep
ML interview questions, project defense, coding prep, and how top companies actually hire.
What gets asked in ML and AI engineering loops, how to explain your project, how to build a portfolio that gets interviews, and the newer rounds on agents and evals. Concrete, with the depth of answer interviewers expect.
7 posts, from beginner to advanced. Each ends with what to read next.
The AI Evals Engineer: A New Role and How to Get It
Evals engineer went from a task to a job title in about two years. Here is what the role actually does day to day, why companies are hiring for it, the skills that matter (and the ones that do not), what the interview looks like, and a portfolio that gets you in.
ML Interview Questions: What Actually Gets Asked
The questions that show up in ML interviews consistently. Not the textbook version - the version that gets asked at top companies, with the depth of answer they expect.
How to Explain Your ML Project in an Interview
Most candidates undersell their ML work. They either go too deep into math no one asked about, or stay too surface-level. Here is the structure that gets you to a compelling story.
ML Coding Interviews: What to Expect and How to Prepare
ML coding interviews test your ability to implement algorithms from scratch and reason about them. Most candidates over-prepare on LeetCode and under-prepare on ML specifics. Here is what matters.
Behavioral Interviews for ML and AI Roles
Behavioral interviews for ML roles are not the same as SWE behavioral interviews. The stories you tell and the qualities they test for are different. Here is how to prepare.
How Top Companies Hire ML Engineers
The hiring process at ML-focused companies follows a pattern. Understanding the process before you enter it makes preparation more efficient and reduces surprises.
Building an ML Portfolio That Gets Interviews
A GitHub profile full of Kaggle notebooks and tutorial follow-alongs will not get you interviews. Here is what a portfolio that actually moves the needle looks like.