Linkedin ML Engineer Interview Guide

LinkedIn ML Engineer interviews in these reports combine implementation work with discussions of machine-learning judgment. One candidate began with a straightforward recruiter phone screen, then completed an AI coding interview on CoderPad and a technical conversation with an intern host manager. That coding round used medium string and graph problems, so coding fluency mattered alongside ML knowledge. The manager discussion went into Graph ML, generative AI, research projects, and resume details.

A separate candidate reported a hiring-manager behavioral conversation plus two ML system-design interviews. That is a useful reason to practice presenting decisions, tradeoffs, and prior work clearly to both technical and managerial audiences. Another candidate reported a single coding interview involving an O(N) monotonic decreasing-stack solution for a constrained subarray-counting problem; be prepared to derive and explain an efficient approach, not only reach an answer.

The reports do not establish one universal sequence, but they consistently point to a mixed ML-engineering assessment. Spend preparation time on writing correct code under pressure, communicating research or production work in depth, and structuring ML system-design answers around the problem you are given.