Interview preparation
Notes for preparing for ML/DS interviews. Organized by the type of round you might encounter in a typical loop — most ML interview loops at large tech companies are some combination of a coding screen, ML fundamentals, ML system design, and behavioral. Not every loop has every round, and phone screens often compress multiple rounds into one.
Coding round
- Leetcode code templates — Python templates for common patterns (two pointers, sliding window, BFS/DFS, binary search, backtracking).
ML fundamentals
Technical Q&A on classical ML, deep learning, and statistics. Expect a mix of conceptual (“explain bias-variance”) and applied (“given this problem, how would you approach it?”).
- General ML questions — supervised/unsupervised, model selection, common pitfalls.
- Deep Learning questions — architectures, training dynamics, modern LLM topics.
- Statistics questions — probability, random variables, classifier combinations.
ML system design
The round that distinguishes senior candidates. You’ll design an end-to-end ML system: clarifying questions → data → model → metrics → deployment → monitoring.
- ML System design — framework for answering system design questions: preliminaries, clarifying questions, metric discussion, and stage-by-stage outline.
Behavioral and soft skills
Technical skills alone don’t close the loop. Meta, Google, and similar companies weight behavioral heavily — often one full round plus scattered behavioral questions in other rounds.
- Behavioral interviews — STAR method, example question templates, how to align answers with IC / tech lead / manager level.
- Questions to ask the interviewers — ~20 curated questions covering environment, team, process, career, and tech stack.
External resources
ML interviews:
- Huyen Chip — ML Interviews Book
- alirezadir/Machine-Learning-Interviews
- labml.ai — annotated deep learning papers
System design:
Mock interview platforms: