Machine Learning System Design Interview, issue 37, May 25, 2026
The Uncertainty Loop Paradox
Senior AI Engineer interview at Meta, and the interviewer asks:
“We have a 10-million sample unlabelled dataset and want to fine-tune a Llama-3 70B model while minimizing manual annotation costs. How do you design an Active Learning loop to selectively label the most uncertain points?”
The hidden trap where calculating predictive entropy on a 70B model completely destroys cluster efficiency, and the lightweight proxy scoring trick that solves it.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.