Interview Prep — Senior ML Engineer
The four loops a senior MLE candidacy runs through, mapped to this track:
| Loop | What's tested | Prep doc | Track foundation |
|---|---|---|---|
| ML system design | end-to-end architecture judgment | 01 | Phases 02–09, 13; system-design/ |
| ML fundamentals + coding | do you actually know how it works | 02 | Phases 04–07, 11 labs |
| MLOps / platform depth | production lifecycle ownership | 03 | Phases 01–03, 08–09, 12 |
| GenAI production judgment | beyond-the-demo LLM engineering | 04 | Phase 10 + LLM track |
| Behavioral (senior bar) | judgment, ambiguity, leadership | 05 | your capstone + work history |
How to use these
- Every answer here is written at the bar the JD sets (7–9+ years): structure first, numbers where they bite, tradeoffs named, limits admitted. Don't memorize prose — internalize the skeletons and regenerate in your own words.
- The coding-adjacent questions are literally the track's labs ("implement NDCG", "build an SRM check", "point-in-time join") — if you built the labs, rehearse by re-deriving, not re-reading.
- For system design, rehearse the 2-minute capstone tour (Phase 13 WARMUP Ch. 5) — interviewers steer; you need entry points, not scripts.
The senior-bar rubric (what interviewers actually grade)
- Clarify before designing — requirements, scale, constraints, the metric.
- Baseline first — the candidate who says "popularity / seasonal-naive / logistic regression first, here's why" outranks the one who opens with a transformer.
- Name the seams — training-serving skew, leakage, feedback loops, label delay. Senior candidates volunteer the failure modes; juniors wait to be asked.
- Quantify — KV-cache math, sample sizes, latency budgets, cost per 1k requests. Two or three well-chosen numbers per hour signal depth.
- Decide — end ambiguous tradeoffs with a recommendation and its trigger for revisiting, not a survey.