Machine Learning Fundamentals: Optimizers, Scaling Laws, and Clustering
Company: Meta
Role: Machine Learning Engineer
Category: Machine Learning
Difficulty: hard
Interview Round: Onsite
Overview: This question evaluates conceptual grasp of core machine learning fundamentals: gradient-based optimizers, neural scaling laws, and unsupervised clustering methods. It tests whether a candidate understands the mechanics and trade-offs behind optimizer design, compute-vs-data allocation, and the mathematical relationship between k-means and Gaussian mixture models, rather than just naming techniques.