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Debug ML pipeline and build text parser

Scale AIMachine Learning Engineer · Technical Screen
MediumMachine Learning EngineerTechnical ScreenData Manipulation (SQL/Python)

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This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.

  • 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).