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Netflix interview notes, page 4 of 20

FAANG / Big Tech

Most of these Netflix write-ups come from Software Engineers (136), then Data Scientists (23) and Product Managers (16). The most reported rounds are the technical phone screen (94), the system design round (48), the product sense round (14) and the behavioral / leadership round (11).

191 interview notes · updated Jul 2026

Netflix·Data Scientist·Onsite - Behavioral / Leadership

Apr 2026
Netflix data scientist interview that leaned hard into the culture memo angle. The whole thing felt like a philosophy seminar more than a technical screen, which I wasn't fully ready for.
  • Netflix's culture memo includes phrases like 'Act like an owner', 'Bias for action', and 'No ego'. Pick three of these and explain where they could be misused or weaponized, then propose a concrete policy-level clarification for each and give a counterexample that would violate it.
  • What specific questions would you ask a hiring manager to figure out whether the culture memo actually reflects how the team operates day to day, especially around decision rights, risk tolerance, how failures are handled, feedback frequency, and performance management?
  • Describe three measurable indicators you'd track in your first 90 days to test whether the company's stated culture actually matches reality. For each, explain how you'd collect the data and what threshold would prompt you to take action.
  • Walk me through a specific time you either upheld or pushed back on a written or stated company culture. What trade-offs did you accept and what was the result?

“This one tripped me up because I came in thinking about technical prep and suddenly I'm doing organizational ethics.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

Apr 2026
Netflix ML engineer interview, one technical question about post-deployment monitoring. Not much to go on but it's a solid question that tripped me up a bit.
  • What metrics do you track after pushing a machine learning model to production?

“I started rattling off accuracy and loss and the interviewer just kind of waited.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round, one big question that sprawled across basically every distributed systems topic you can think of. Felt like they wanted to see how far you could go before running out of ideas.
  • Design a global configuration deployment system that supports staged rollouts, automatic rollback on health regressions, regional ordering, and observability. Also cover how clients read config and how you handle a bad config that's already in production.

“This question just kept expanding.”

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Netflix·Software Engineer·Hiring Manager Screen

Apr 2026
Interviewed for a bizops role at Netflix and got hit with a user behavior diagnostic question that felt more analytical than I expected for the role.
  • How would you go about investigating why 10% of Netflix users are inactive?

“I started by trying to define 'inactive' which felt like the right move but I probably spent too long on it.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026
Netflix coding screen, graph problem that looks straightforward until you realize you need to think in terms of topological sort and DAG levels. The follow-up they mentioned is a harder variant with a per-semester course cap, which I hadn't prepped for.
  • Given n courses labeled 1 through n and a list of prerequisite pairs, find the minimum number of semesters needed to complete all courses if you can take any number of courses per semester (as long as prerequisites are done). Return -1 if it's impossible.
  • Follow-up: how would you solve the same problem if you could only take at most K courses per semester?

“My first instinct was BFS and I went with it, which was right, but I fumbled explaining why the number of BFS levels equals the answer.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round for a software engineer role. The whole thing was basically one meaty cache design problem that took up the entire session. Felt like it went okay but there were definitely parts I fumbled through.
  • Design a weighted cache with a fixed total weight capacity. Each item has a key, value, and positive weight. Support get (with recency update) and put (with eviction of heaviest items when over capacity, breaking ties by least-recently-used).

“I knew LRU cache cold so my first instinct was to just reach for a doubly linked list plus a hashmap and call it a day.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

Apr 2026
Netflix PM interview with a classic 0-to-1 product question. Not a lot of context in what was shared, but the question itself is the kind that can go sideways fast if you don't have a real story ready.
  • Walk me through a time you launched a product from scratch. How did you take it from zero to something real?

“This is the question that exposes whether you actually built something or just managed a roadmap someone else handed you.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round, ads infrastructure focus. The question was dense and covered a lot of ground fast, felt like they wanted to see if you'd panic or actually structure your thinking under pressure.
  • Design a frequency capping system for ads that limits how often a user sees the same ad or campaign within configurable time windows like hourly, daily, and weekly. Walk through data modeling, counter storage choices, per-user and per-campaign cap policies, distributed counting accuracy, how it integrates with ad serving, and where you land on consistency vs latency.

“This one sprawled.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

May 2026
Netflix ML engineer screen, one deep technical question about CNNs vs traditional nets. Pretty focused interview, no fluff.
  • How do convolutional neural networks differ from traditional neural networks when it comes to processing image data?

“I started with spatial locality and parameter sharing, which felt right, but I fumbled a bit explaining why fully connected layers are actually bad for images at scale.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix SWE screen, pretty standard array/hashing territory. Nothing too wild but they did probe on variants which I wasn't fully prepped for.
  • Given an array of strings, determine whether any value appears more than once. Return true if a duplicate exists, false if all elements are distinct.

“Jumped straight to the hash set approach, O(n) time and space, insert each element and bail early on a collision.”

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