Katkat
Back

Design LinkedIn Learning course recommendations

LinkedInMachine Learning Engineer · Onsite
MediumMachine Learning EngineerOnsiteML System Design

Last updated

The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.

Design a mini ML system to recommend LinkedIn Learning courses to a user.

Product goal:

  • Recommend courses that help the user succeed in their job search and/or current role.

Available signals (examples):

  • Jobs the user applied to, jobs the user viewed/saved, jobs recommended to the user.
  • User profile (title, skills, seniority, industry), past course consumption, dwell/completion, search queries.
  • Course metadata (title, description, skills taught, difficulty, duration).
  • Job post text (description, requirements).

Requirements:

  1. Propose an end-to-end architecture (data collection → feature generation → candidate generation → ranking → serving).
  2. Explicitly describe how you would extract useful features from job post text (e.g., skills/requirements). Mention at least one non-LLM and one LLM-based approach.
  3. Discuss training data/labels, offline and online evaluation metrics, and key failure modes (cold start, bias, feedback loops).
  4. Briefly describe how you would incorporate business constraints (freshness, diversity, exploration, compliance).