Scale AI·Machine Learning Engineer·Onsite - Coding / Algorithms
- Given a dataset, perform data preparation tasks as part of an ML coding assessment.
- Debug a provided ML code snippet and identify all bugs present.
“This part felt more manageable.”
30 Machine Learning Engineers have shared notes from their Scale AI interviews, with the questions they were asked in each round. That is 38% of the 79 Scale AI interview notes on Screna AI.
30 Machine Learning Engineer interview notes
“This part felt more manageable.”
“I knew this cold so it felt fine at first, but they kept pushing on the complexity angle.”
“The bugs themselves weren't that hard once I found them.”
“Picking the right project matters more than I expected.”
“Pretty open-ended so I just started rambling about tokenization and a text classification project I'd done.”
“I started with data collection and kind of rambled my way through preprocessing, augmentation, model training, evaluation, and deployment without really tying it together.”
“The starter code had softmax and test cases already written, which honestly helped a lot.”
“This one went longer than I expected.”
“This question sounds like a warmup but it absolutely is not.”
“This was part 3 of a multi-part coding problem.”