Design an ML job scheduler
Company: Netflix
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: easy
Interview Round: Onsite
Design a system that accepts user-submitted machine learning jobs and schedules them across multiple compute clusters containing heterogeneous CPU and GPU resources.
The system should:
- accept job submissions from users
- choose an appropriate cluster for each job
- allocate CPU and GPU resources efficiently
- execute jobs and track their status
- make good overall use of shared compute capacity
Discuss the architecture, scheduling strategy, resource model, fairness, reliability, and how you would handle heterogeneous clusters and different job types.
Quick Answer: This question evaluates competency in ML systems design, distributed resource scheduling, and cluster resource management for heterogeneous CPU and GPU environments.