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Netflix interview notes, page 10 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·Software Engineer·Technical Phone Screen

May 2026
Netflix coding round, one question the whole time. Graph stuff, specifically topological sort. Not the hardest topic but I definitely fumbled some of the implementation details under pressure.
  • Given a directed acyclic graph, return a valid topological ordering of its nodes. Handle the case where a cycle exists. You can use either a BFS-based in-degree approach or DFS with post-order traversal.

“I went with the in-degree BFS approach because I'd drilled it recently and felt more confident there.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

May 2026
Netflix PM interview, single question focused on how AI could improve their Help/support experience. Pretty open-ended and I wasn't totally sure how deep they wanted me to go on the technical side versus the product side.
  • How would you use AI to improve Netflix's Help and customer support experience?

“I started by trying to map out where users actually get stuck, like finding the right help article, troubleshooting playback issues, or figuring out billing stuff.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

May 2026
Netflix system design round for a software engineer role, focused entirely on building a thread-safe in-memory key-value store for a multi-threaded AI platform. The question had a lot of sub-parts and went pretty deep into concurrency, testing, and production concerns.
  • What internal data structure would you use to back a thread-safe key-value store, and why?
  • Walk through different synchronization strategies for a shared key-value store: single global lock, per-key locks, lock striping, or a built-in concurrent map. What are the trade-offs?
  • Implement put, get, and delete for a thread-safe key-value store in Python, which has no built-in concurrent map.
  • How would you test a concurrent key-value store for correctness and race conditions? Think unit tests, stress tests, and any tooling.
  • If this key-value store were used in production, what additional concerns would you address beyond basic correctness?

“Started with a plain hash map and explained why, which felt almost too obvious.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix SWE interview with a system design coding problem around building a command/undo system. The question had more depth than I expected once the tag-based undo requirement came in.
  • Design and implement a command/undo system with two operations: execute(command, tags) which records a command along with a list of tags, and undo(tag=None) which undoes either the most recent command or the most recent command associated with a given tag. Discuss your data structure choices and time complexity for both operations.

“I started with a plain stack for the no-tag case, which was fine, but the tag-based undo is where things got interesting.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix coding screen, one algorithmic problem, pretty standard stuff. The sliding window thing is a classic but I still managed to fumble the edge cases a bit before getting it sorted.
  • Given a string, find the length of the longest substring with no repeated characters.

“Knew immediately it was a sliding window problem, which felt good.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix SWE coding round, one main problem with follow-up variations. The core question was a sliding window thing I'd seen before but the generalized framing threw me off a little.
  • Given a list of arbitrary elements (strings, integers, etc.), find the length of the longest contiguous subarray where all elements are distinct. Optionally return the subarray itself.

“I recognized it as a sliding window problem pretty fast, which helped.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

May 2026
Netflix system design round, one meaty question about ad frequency capping that spiraled into a pretty deep discussion on distributed counters and consistency trade-offs. Left feeling like I covered the basics but probably didn't go far enough on the cross-datacenter stuff.
  • Design a frequency capping service for online ads, where a user should not be shown the same ad or campaign more than N times within a configurable time window (per hour, per day, or per campaign lifetime). Cover the real-time cap check on the ad-serving path, reliable impression recording, storage choices, and consistency trade-offs at scale.

“I started with Redis and sliding window counters which felt right, but then the interviewer pushed on what happens across datacenters and I kind of stumbled.”

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Netflix·Data Scientist·Technical Phone Screen

May 2026
Netflix data scientist interview with a SQL heavy question around consecutive ordering streaks. Pretty niche problem, felt like it was designed to filter out people who only know basic aggregations.
  • Given an orders table with user IDs and order dates, find the longest streak of consecutive calendar days on which each user placed at least one order.

“I knew it was a gaps-and-islands type problem the second I saw it, which helped.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix coding screen, one question about detecting duplicates in a user's watch history. Pretty straightforward stuff, felt more like a warmup than a real filter.
  • Given a chronological list of episode IDs from a single user's watch history, write a function that returns True if any episode appears more than once, and False if all IDs are unique.

“Basically a set lookup problem.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

May 2026
Netflix ML engineer screen, pretty deep on fine-tuning fundamentals. One meaty technical question that took up most of the time, felt like they wanted to see how far you could go with it rather than just checking a definition.
  • Explain LoRA as an approach to fine-tuning large pre-trained models efficiently. Walk through the mechanics, where it gets applied, what you actually gain from it, and what knobs you're tuning when you use it.

“This started straightforward and then got uncomfortable fast.”

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