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An interview prep path for ML Engineer loops. ML loops are usually a hybrid: a coding round, a Python/algorithms round, an ML system design round, and behavioral. This path sequences each leg with the existing content and pads the system-design ramp with the topics that show up most (search, analytics pipelines, notifications).
ML coding rounds lean Python and are heavier on data manipulation than classic LeetCode. Refresh both - the language idioms and the algorithmic patterns.
ML coding interviews often look like 'here is a stream / a matrix / a list of records, do X efficiently'. These problems lock in the patterns.
Even pure ML roles ask a generalist system design question. Build the vocabulary first, then move into ML-specific designs.
These three walkthroughs map onto the most-asked ML system designs: search and ranking (autocomplete), analytics-pipeline shape (feed), and event-fanout (notifications).
ML behavioral rounds screen for technical depth (can you actually defend the decisions in your last project?) and end-to-end ownership (did you ship it, or just train a model?).
LLM and prompt-engineering vocabulary, plus the SQL sheet - feature work and analysis are both SQL-heavy in practice.
ML engineering loops usually include a data round. Aggregation and window functions over event tables are the shapes that come up most.
21 role-targeted paths are live, from new-grad and backend through SRE, security, data and engineering management. If you have a role you want covered, let us know.
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