LinkedIn·Machine Learning Engineer·Onsite - System Design / Architecture
May 2026
LinkedIn MLE interview that went deep into NLP and data mining territory. The main question was a full system design for how LinkedIn's Skills feature actually works under the hood, and they wanted you to build it from scratch, not just wave your hands at a language model.
- Design the full ML pipeline for LinkedIn's Skills feature as a data-mining problem: how would you collect candidate skill tokens, parse and tokenize resumes, construct training data with positive and negative examples using weak supervision, train a classifier to predict whether a token is a skill, evaluate it, and keep the skill taxonomy updated over time?
“This one sprawled in every direction and I kept second-guessing where to start.” The rest of the author's notes on Machine Learning Engineer interview at LinkedIn, Onsite - System Design / Architecture round, covers how they worked through the question, what the panel pushed back on, and what they would do differently.
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