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Netflix interview notes, page 17 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

Jun 2026
Netflix coding round, one problem the whole session. It sounds like a grouping problem but there are a few wrinkles that slow you down if you're not careful.
  • Given a map of user IDs to their movie-watching histories (ordered lists of movie IDs), group users who share the exact same set of last K movies watched. Only return groups with at least two users. Describe your algorithm and its time and space complexity.

“The core idea isn't that hard once you see it: take the last K movies for each user, throw them into a set, sort the set to get a canonical key, then use a hashmap to bucket users by that key.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Netflix SWE interview with a strong engineering focus. The questions pushed past surface-level design into real production scenarios, which I wasn't fully prepared for.
  • If a server goes down in production, how do you handle the cache to prevent data loss?

“This is where I started sweating.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

Jun 2026
Netflix ML engineer screen, one meaty coding question about parsing model parameter keys into a nested structure. Felt like a mix of pure Python and systems thinking, which I wasn't fully expecting.
  • Given a flat list of model parameter keys like 'layer1.attention.q_proj.weight', write a Python function that groups them into a nested dictionary by layer index, placing components like attention and mlp under the correct layer. Define your grouping logic, handle malformed or duplicate keys, and walk through time and space complexity.

“I started by splitting on dots and pulling the layer prefix out, which felt clean, but then they asked what happens with keys that don't follow the pattern and I kind of stumbled.”

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Netflix·Software Engineer·Technical Phone Screen

Jun 2026
Phone screen for a Netflix SWE role, timed coding problem around cache design. The interviewer clearly swapped some keywords from a canned prompt, which threw me off at first. Pretty focused on implementation details and tradeoffs around cleanup strategy.
  • Design a cache where each item has a fixed TTL. Start with the simplest possible implementation, then extend it to support cache cleanup. The get() method is on the hot path, so avoid doing cleanup there. Assume a separate background process handles eviction.

“The prompt felt slightly off when I read it, like a word or two had been swapped from some other version of the question.”

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Netflix·Software Engineer·Onsite - Coding / Algorithms

Jun 2026
Netflix software engineering coding round. The main problem was implementing a command undo system, and a big chunk of the time got eaten up by complexity analysis.
  • Design and implement a command undo system.

“We spent way longer on the time complexity discussion than I expected.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Netflix system design round, one question the whole time. They wanted a full implementation of a command executor with undo support, plus a design discussion about extending it. Pretty deep for a single question but I actually liked the format.
  • Design and implement a class that supports executing commands and undoing the most recent one. How would you extend it to support redo, grouped/transactional commands, and commands that can't be undone?

“I knew the Command pattern going in but I still fumbled the undo stack explanation at first.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

Jun 2026
Netflix system design round focused on ad tech internals, specifically how the DSP side models and tracks an order. Pretty niche stuff if you haven't worked in programmatic advertising before.
  • Walk through how the DSP side of an ad platform tracks an order. Include all the relevant config types, and keep in mind the order is direct sold demand with no open auction involved.

“This one exposed how much my mental model was built around RTB and open auction flows.”

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Netflix·Data Scientist·Technical Phone Screen

Jun 2026
Netflix data scientist interview centered entirely on experiment design for a streaming feature with network effects. Pretty intense technically, lots of follow-ups, and I left genuinely unsure how I did.
  • How would you select primary and guardrail metrics for an A/B test on a new streaming feature?
  • What threats do network effects pose to a standard A/B test, and how would you address them?
  • Can you explain SUTVA and why it's relevant to this experiment?
  • Walk me through how you'd design and interpret a multivariate test on title artwork.
  • How would you measure heterogeneous treatment effects across different user segments?

“I went straight to engagement metrics like watch time and retention, which felt right, but I fumbled a bit when they pushed on what guardrails I'd use.”

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Netflix·Data Scientist·Onsite - System Design / Architecture

Jun 2026
This was a deep ML system design round for a notifications modeling role. The question was essentially one giant end-to-end case study and they just kept pulling on threads for the full hour. Felt more like a technical dissertation defense than a standard interview.
  • How would you define training labels for a purchase propensity model when historical notifications have already influenced user behavior? How do you handle post-treatment leakage, multiple exposures, and the choice between intent-to-treat vs. treated-only labels?
  • What behavioral and contextual features would you build for this model, and how do you prevent target leakage, enforce time-consistent feature joins, and reduce training-serving skew?
  • How would you handle class imbalance in this purchase propensity model, and what calibration approach would you use? How do you monitor and recalibrate over time?
  • How would you evaluate this model offline? What metrics would you use, how would you construct your train/validation splits, and what slice analyses matter?
  • Without a randomized experiment in the historical logs, how would you estimate the incremental revenue impact of sending notifications to the top 20% of scored users?
  • How would you design the online experiment to validate this model in production? What are your guardrails, ramp criteria, traffic split logic, and how do you detect feedback loops or distribution shift?
  • How would you handle cold start for new users and new items on day zero, and how would you backfill training labels as you accumulate data?

“This is where I spent the most time and also where I stumbled most.”

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Netflix·Software Engineer·Technical Phone Screen

Jul 2026
Netflix SWE interview with a bitmask DP problem that looked like a scheduling puzzle but had a lot more going on under the hood. Pretty algorithmic heavy, not much else to say about the round itself.
  • You have n courses labeled 1 through n, a list of prerequisite pairs, and an integer k representing the max courses you can take per semester. A course can only be taken if all its prerequisites were completed in strictly earlier semesters. Find the minimum number of semesters to finish all courses, or return -1 if it's not possible.

“I knew this was a bitmask DP problem pretty quickly, which felt good for about 30 seconds until I started fumbling the state transitions.”

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