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Netflix interview notes, page 11 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
Phone screen for a software engineering role at Netflix. One coding problem, pretty focused, no fluff.
  • Implement a print cache that suppresses duplicate print calls: if a value has already been printed within the past 10 seconds, skip it.

“Seemed simple at first and I almost said 'just use a hashmap' before catching myself.”

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Netflix·Software Engineer·Onsite - Coding / Algorithms

May 2026
Netflix SWE problem-solving round that basically fell apart in the clarification phase. The interviewer kept pushing back on edge cases for a complex JSON matching problem, and by the time we got to actual coding there was barely 30 minutes left.
  • Given a name-to-config metadata mapping and a JSON query, return all configs that match the query criteria. Metadata values can be nested: key to array, key to object with further nested arrays. Unspecified fields use defaults. Implement this efficiently using an inverted index.

“Brute force was the obvious first move and I said so, but she shut that down fast and asked for an inverted index approach.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

May 2026
ML fundamentals screen for a Netflix MLE role. Two topics back to back: loss functions and LoRA. No coding, just whiteboard reasoning and they pushed hard on the math.
  • Compare MSE and cross-entropy loss. When is each appropriate, what probabilistic assumption does each correspond to, and why is cross-entropy preferred over MSE for classification with a sigmoid or softmax output?
  • Derive the gradient of MSE and cross-entropy with respect to the pre-activation logit for a sigmoid output, and explain precisely why MSE's gradient can stall when the model is confidently wrong.
  • Explain the core mechanism of LoRA: how the weight update is parameterized, what is frozen versus trained, and how the adapter is handled at inference.
  • For a 4096 by 4096 attention projection matrix with rank 8, what fraction of that layer's parameters does LoRA actually train?
  • What do the LoRA hyperparameters rank, alpha, and dropout control, and how would you approach tuning them?
  • How does LoRA compare to other parameter-efficient fine-tuning approaches like adapters, prefix tuning, or QLoRA, and when would you choose one over another?

“I knew the high-level answer but fumbled when they pushed on the likelihood framing.”

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Netflix·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at Netflix, one question about the recommendation engine. Pretty focused session, no fluff.
  • How would you measure the success of Netflix's recommendation engine?

“I started with engagement metrics and the interviewer kind of just waited, like they wanted more.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix coding round, one meaty algorithmic problem about finding pairs of users with overlapping watch histories. The problem had a follow-up asking about efficiency at scale, which is where things got interesting.
  • You have a dictionary mapping user IDs to their ordered movie-watching histories. Given integers K and M, find all pairs of distinct users whose most recent K movies share at least M movies in common. Design an efficient algorithm and analyze its time and space complexity.

“The naive approach is obvious: for each user grab their last K movies as a set, then do pairwise intersections across all users.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix technical phone screen for a software engineer role, pretty much one meaty coding problem with a follow-up that pushed into system design territory. The main question was interesting but the streaming follow-up is where things got uncomfortable fast.
  • Given an array of show names representing a user's viewing history, find the start and end indices of the longest contiguous subarray with no repeated show names (case-insensitive). Optimize for O(n) time and O(min(n, m)) space where m is the number of distinct shows.
  • Adapt your solution to a streaming API that yields one show name at a time. You must always be able to report the current longest unique-name window. Describe the data structures, the update complexity per element, and the memory bounds.

“Sliding window with a hashmap, pretty standard once you see it.”

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Netflix·Software Engineer·Onsite - System Design / Architecture

May 2026
Netflix system design round focused entirely on an ads frequency-cap service. The interviewer was pretty deliberate about separating the read and write paths, which ended up being the whole crux of the conversation. Solid problem but it goes deep fast.
  • Design an ads frequency-cap service that decides, given an ad request, whether a user has already seen a particular ad, campaign, or advertiser more than the configured cap within a time window. The read path (cap check at serve time) must hit sub-10ms p99, and the write path (impression logging) must be designed and scaled independently.

“This question is deceptively big.”

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Netflix·Data Scientist·Technical Phone Screen

May 2026
Interviewed at Netflix for what seemed like a data science or ML role. Just one question from what I can tell, pretty foundational stuff.
  • What is the difference between classification and regression?

“Bread and butter ML question.”

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Netflix·Software Engineer·Technical Phone Screen

May 2026
Netflix coding screen, one algorithmic question the whole time. The problem looked deceptively simple until you actually had to think about scaling it past brute force.
  • Given a list of lowercase show name strings, count the number of unique unordered pairs where the two strings share no common character.

“Started with the obvious O(n^2) loop comparing every pair character by character, which they accepted but clearly wanted more.”

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Netflix·Machine Learning Engineer·Technical Phone Screen

May 2026
Netflix ML Engineer interview, technical phone screen that went pretty deep into transformer internals. One question but it had a lot of layers to it and I felt like I was being tested on whether I actually understood the math or just memorized the formula.
  • In scaled dot-product attention, why do we divide the dot product of Q and K by the square root of the key dimension before applying softmax?

“I knew the answer existed but fumbled the variance argument at first.”

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