Debug ML pipeline and build text parser
Company: Scale AI
Role: Machine Learning Engineer
Category: Data Manipulation (SQL/Python)
Difficulty: Medium
Interview Round: Technical Screen
- Given raw text files with noisy formatting, implement a robust parser that outputs structured examples; handle delimiters, quoting/escaping, encodings/Unicode, missing fields, and malformed lines, and describe how you would test it.
- In a provided ML project (data loading, preprocessing, training, evaluation), identify and fix three defects (e.g., index off-by-one in tokenization, train/test leakage, incorrect loss reduction, nondeterministic seeding, or shape mismatches). Explain your rapid debugging approach (stack traces, assertions, binary search logging, minimal repros).
- Describe how you would validate the fixes under a 60-minute time limit (unit tests, end-to-end run, metrics sanity checks, and regression guards).
Quick Answer: This question evaluates skills in robust text parsing, data cleaning, debugging ML pipelines, and rapid validation, covering competencies such as handling delimiters/quoting/encodings, managing missing or malformed fields, identifying defects in data loading/preprocessing/training, and designing tests under a time constraint.
