Netflix Interview Guide

Netflix runs the most senior-weighted loop here. It hires few people, at high levels, and the interview reflects that: less algorithmic puzzle-solving, far more on judgement, autonomy, and whether you can operate without process around you. The published culture memo is not marketing — it is close to a rubric, and reading it is genuine preparation.

Expect practical, systems-flavoured problems over contest-style algorithms, and expect to justify decisions rather than derive optimal complexities. This page’s problem list is derived from pattern families rather than reports — there is very little reliable crowd data on Netflix.

Crowd-reported, not published

No company publishes its question bank. The round structure and the bar below are corroborated by recruiters and candidates; the problem list is crowd-reported, stale, and biased toward whoever posted it. Treat the pattern families as the durable signal — which families a company leans on changes far more slowly than which individual problems it uses.

Hires senior and above almost exclusively, and weights real engineering judgement far above puzzle-solving. The algorithm bar is real but secondary to how you reason about systems and trade-offs.

The loop

Recruiter + hiring manager 2× · 45 min Seniority, autonomy, culture-memo alignment

Technical 2-3× · 60 min Practical coding, often on a realistic problem rather than a puzzle

Deep dive 1-2× · 60 min Architecture and past decisions in detail

The bar

Explain trade-offs you rejected, not just the design you chose. The culture memo's "candour" is applied to interviews too — hedging reads badly.

What they lean on

This is the durable part. Which pattern families a company favours is far more stable than which individual problems it uses, so prepare in this order:

1. hash-tables
2. design-trackers-and-feeds
3. top-k-elements
4. simulation-and-stateful-iteration
5. sorting-with-custom-comparators

Quirks worth knowing

• Very few rounds, each with high weight. There is little room to recover from one bad round.
• Expect open-ended problems with no single right answer, judged on how you narrow them.
• Compensation is single-figure salary with no equity refresh cycle, so negotiation dynamics differ from the rest.

Reported problems

Read this before you trust the list. No company publishes its question bank. Every "asked at Netflix" list in circulation — including this one — is aggregated from candidate reports, is months to years out of date, and over-represents whoever bothered to post. Use it to choose an order to practise in. Do not use it as a question bank, and do not assume a problem missing from it is safe to skip. The list below is currently derived from Netflix's pattern families rather than from per-problem tags, which is the more honest basis while the tag data is thin.

32 problems

7 easy23 medium2 hard

AllEasyMediumHard

Unsolved onlyExportImportReset

• ○1Two SumeasyNeetCode 150Blind 75LeetCode Top Interview 150
• ○217Contains DuplicateeasyNeetCode 150Blind 75
• ○346Moving Average from Data Streampremiumeasy
• ○359Logger Rate Limiterpremiumeasy
• ○657Robot Return to Origineasy
• ○703Kth Largest Element in a StreameasyNeetCode 150
• ○933Number of Recent Callseasy
• ○49Group AnagramsmediumNeetCode 150Blind 75LeetCode Top Interview 150
• ○347Top K Frequent ElementsmediumNeetCode 150Blind 75
• ○6Zigzag ConversionmediumLeetCode Top Interview 150
• ○36Valid SudokumediumNeetCode 150LeetCode Top Interview 150
• ○179Largest Numbermedium
• ○215Kth Largest Element in an ArraymediumNeetCode 150LeetCode Top Interview 150
• ○289Game of LifemediumLeetCode Top Interview 150
• ○355Design TwittermediumNeetCode 150
• ○362Design Hit Counterpremiummedium
• ○406Queue Reconstruction by Heightmedium
• ○451Sort Characters By Frequencymedium
• ○692Top K Frequent Wordsmedium
• ○763Partition LabelsmediumNeetCode 150
• ○846Hand of StraightsmediumNeetCode 150
• ○853Car FleetmediumNeetCode 150
• ○874Walking Robot Simulationmedium
• ○937Reorder Data in Log Filesmedium
• ○973K Closest Points to OriginmediumNeetCode 150
• ○981Time Based Key-Value StoremediumNeetCode 150
• ○1029Two City Schedulingmedium
• ○1041Robot Bounded In Circlemedium
• ○1352Product of the Last K Numbersmedium
• ○2013Detect SquaresmediumNeetCode 150
• ○68Text JustificationhardLeetCode Top Interview 150
• ○354Russian Doll Envelopeshard

Pitfalls

• Preparing like it is a LeetCode loop. The algorithmic content is real but secondary. Judgement, ownership, and communication carry more weight.
• Not reading the culture memo. It functions as a rubric for the behavioural conversation, and candidates who have engaged with it are visibly better prepared.
• Answering as an implementer rather than an owner. “I was told to build X” is a weak answer at this level. What did you decide, and what did you decline to do?
• Expecting a junior-friendly bar. Few hires, high levels. If your experience does not yet include owning ambiguous work end to end, this is the hardest loop on this page.

Interview follow-ups

They ask	What they’re checking	The answer
“What did you decide not to do?”	Judgement and prioritisation	A real trade-off you made, the thing you deliberately dropped, and why. Senior signal is what you declined, not just what you shipped.
“How did you operate without a process?”	Autonomy	A concrete case where you set the direction. Netflix screens for people who do not need scaffolding.
“Would you push back on this requirement?”	Candour, per the culture memo	Yes, with the reasoning and the data — and then say what you would do if overruled. Disagreement is expected here; disagreement without a follow-through plan is not.
“Design something practical” (rather than a puzzle)	Systems judgement	Talk trade-offs, failure modes and operational cost rather than optimal asymptotics. This is the shape the round usually takes.

Recall card

• Most senior-weighted loop here. Few hires, high levels, judgement over puzzles.
• Read the culture memo. It is close to a rubric for the behavioural conversation.
• Answer as an owner, not an implementer — what you decided, and what you declined.
• Practical and systems-flavoured problems over contest algorithms. Trade-offs beat asymptotics.
• Least reliable crowd data of any company here — the list below is pattern-derived, so weight the families.