unique interview compared to other AI companies: 1. system design: design an annotation pipeline that can handle 1M images/day with quality control 2. ML: train a model to predict annotation quality from annotator metadata 3. coding: implement a consensus algorithm for multi-annotator labels 4. product sense: given a new annotation task type, how would you bootstrap quality with zero labeled data? the product sense round was the hardest. bootstrapping quality from scratch is a genuine chicken-and-egg problem

Scale AI ML engineer - annotator pipeline design

908cde3f

unique interview compared to other AI companies: 1.

dc7a823e

the bootstrap question is fascinating. the answer is probably: start with a small set of expert annotations as gold standard, use that to calibrate a larger pool of annotators, then iteratively expand

f11c8dc8

scale AI interviews are underrated in difficulty. the problems are real and the interviewers have deep domain expertise