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How would you support ML stakeholders?

NetflixMachine Learning Engineer · Onsite
EasyMachine Learning EngineerOnsiteBehavioral & Leadership

Last updated

This question evaluates skills in ML infrastructure, stakeholder management, production monitoring, model iteration optimization, and data governance when collaborating with data science partners.

You are an ML infrastructure engineer working closely with a data scientist stakeholder. Discuss how you would handle the following situations in a practical, collaborative way:

  1. The stakeholder says model iteration is too slow. How would you identify the bottlenecks and improve the iteration loop?
  2. A model has been launched, but its production performance is worse than expected. How would you respond?
  3. The team wants to train a model but cannot access the necessary data because of approval, ACL, or governance friction. How would you help improve the process?

Your answer should show how you balance infrastructure thinking with an understanding of modeling challenges, how you communicate with non-infra partners, and how you avoid overpromising in imperfect real-world systems.