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Netflix interview notes, page 2 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·Recruiter / HR Screen

Apr 2026
Interviewed for a SWE role at Netflix, and the motivational question came up early.
  • Why do you want to work at Netflix?

“Blanked a little on this one, which is embarrassing because you'd think I'd have a ready answer.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round focused entirely on ads frequency capping, which sounds narrow until you realize how many rabbit holes it opens up. Walked out feeling like I'd touched maybe 60% of what they wanted to cover.
  • Design an ads frequency capping service that limits how many times a user sees a specific creative or campaign within configurable time windows, such as 3 times per day per campaign or 10 times per week across an advertiser's entire catalog.
  • How does a frequency capping service differ from a generic rate limiter, and what extra components does it require specifically for advertiser configuration and onboarding?
  • How would you handle real-time frequency cap decisioning at very high QPS while keeping latency low?
  • How would you design the configuration API for advertisers to set and update frequency caps, including validation and propagation across the system?
  • What reporting data would you need to expose for frequency capping, and how do privacy and compliance constraints affect your design?
  • How do you handle failure scenarios in the frequency capping service, for example if the counter store becomes unavailable?

“I started with the counter store and storage design, which felt natural, but the interviewer kept pulling me toward identity resolution before I was ready.”

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Netflix·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Netflix behavioral round for a software engineering role. One question, but it had a lot of layers to it and I felt like I was being watched for how I structured the story as much as what actually happened.
  • Tell me about the biggest mistake you've made at work. Walk through what happened, the impact it had technically and on the team or business, how you recovered, what you communicated and to whom, and what actually changed in how you work afterward.

“This is a lot to cover in one answer and I underestimated that going in.”

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Netflix·Data Scientist·Technical Phone Screen

Apr 2026
Netflix data scientist interview with a surprisingly algorithmic coding question. The problem was more CS-heavy than I expected for a DS role, felt like I was interviewing for a software engineer position halfway through.
  • Given an array of integers, find the length of the longest strictly increasing contiguous subarray you can get by removing at most one element from it. Return the length and the 0-based index pair [l, r] of that subarray in the original array. If there are ties, return the lexicographically smallest pair. Must run in O(n) time with O(1) extra space.

“This wrecked me a little.”

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Netflix·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026
Netflix behavioral round for a software engineering role, focused entirely on feedback situations. Two questions, both probing how you handle uncomfortable conversations, either giving or receiving criticism. The bar felt high for specificity.
  • Describe a time you gave constructive feedback to a teammate or a cross-functional partner like a PM, data scientist, or designer when something wasn't going well. What was the situation, how did you approach it, and what came of it?
  • Tell me about a time you received critical feedback. How did you react, and what did you actually change as a result?

“This one is trickier than it looks because Netflix culture is pretty direct and they want to see that you actually said something hard, not that you scheduled a meeting and danced around it.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round focused entirely on ad platform data modeling. One long question, lots of depth expected, and I felt the pressure of having to cover schema design, constraints, and query patterns all in one go.
  • Design the entity schema for an ad platform from top to bottom: Account, Advertiser, Campaign, Ad Group, Creative, and event-level data like impressions and clicks. Cover fields, primary keys, foreign keys, constraints like budget caps and targeting rules, and explain how the schema supports targeting, bidding, billing, and reporting.

“This is a beast of a question and I underestimated how far they wanted me to go.”

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Netflix·Machine Learning Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix ML infra interview that leaned heavily on the collaboration angle, not just the technical side. Three scenarios, all framed around working with a data scientist stakeholder, and the pressure was on showing you understand both worlds without pretending infra can fix everything.
  • A data scientist says model iteration is too slow. How do you find where the bottleneck actually is, and what do you do about it?
  • A model launched and production performance is worse than expected. Walk me through how you respond.
  • The team needs data to train a model but it's locked behind access approvals and governance processes. How do you help move things forward?

“I started talking about profiling training jobs and caching preprocessed data, which was fine, but I think I jumped to solutions too fast.”

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Netflix·Software Engineer·Technical Phone Screen

Apr 2026
Netflix coding screen focused entirely on building an inverted index from scratch, including phrase search support. The question had more moving parts than I expected and the discussion about position lists caught me a bit flat-footed.
  • Given a collection of documents, design and implement an inverted index that supports both single-word search (return document IDs containing the word) and phrase search (return document IDs where a sequence of words appears consecutively and in order). Walk through tokenization, case-folding, your position-list structure, and the complexity of each operation.

“I got the single-word part out pretty fast, map from token to set of doc IDs, easy.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix system design round focused entirely on ads infrastructure. One big question, lots of surface area, and I felt like I was playing catch-up the whole time.
  • Design the data model for an ads demand system, covering advertisers, campaigns, line items, ads, targeting criteria, bids, budgets, and impression/click events. Walk through your fact vs dimension table choices, how you'd handle slowly-changing dimensions for campaigns, your partitioning strategy for event data at impression scale, and how the schema supports queries like demand forecasting, fill rate analysis, and pacing reports.

“This is a beast of a question and I underestimated how interconnected the pieces are.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Apr 2026
Netflix virtual onsite for a software engineering role. One system design question, focused on rate limiting and frequency capping. Pretty niche problem if you haven't thought about ad delivery or notification systems before.
  • Design a frequency cap system that limits how often a user is shown a particular piece of content or notification within a given time window.

“This one requires you to think about scale pretty fast.”

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