Katkat

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Netflix interview questions

97 practice questions in the style of Netflix's interviews, covering Machine Learning, Data Manipulation (SQL/Python), Coding & Algorithms, Behavioral & Leadership, Statistics & Math, ML System Design, System Design, Analytics & Experimentation, Software Engineering Fundamentals.

Questions97

9 easy63 medium25 hard

Your progress0/97

Netflix interview questions by the numbers

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Katkat has 97 Netflix interview questions. The largest topics are Coding & Algorithms (31%), System Design (23%) and Software Engineering Fundamentals (12%). By difficulty: 9% easy, 65% medium and 26% hard. The most common round is Onsite (47%). Frequent concepts include System Design, Distributed Systems, Caching and Data Structures.

Topics

Difficulty

  • Easy9% · 9
  • Medium65% · 63
  • Hard26% · 25

Interview rounds

  • Onsite47% · 46
  • Technical Screen40% · 39
  • Other5% · 5
  • HR Screen4% · 4
  • Online Assessment3% · 3

Roles

  • Software Engineer64% · 62
  • Data Scientist21% · 20
  • Machine Learning Engineer11% · 11
  • Data Engineer4% · 4

Frequent concepts

  • System Design12% · 12
  • Distributed Systems7% · 7
  • Caching6% · 6
  • Data Structures6% · 6
  • Concurrency5% · 5
  • Scalability5% · 5

Most mentioned in interview experiences

  1. 1.Design a Job Scheduler for ML Training and Batch Inference on a Shared Cluster2 reports
  2. 2.Design viewport dedup for Netflix home page2 reports
  3. 3.Model data for an ads platform2 reports
  4. 4.Movie Playlist With Live Score Updates and No Repeats per Loop, Plus Thread Safety2 reports
  5. 5.Answer core probability and statistics questions1 report
  6. 6.Answer Netflix Culture Screen Questions1 report
  7. 7.Compute Earliest Completion Times1 report
  8. 8.Compute ITT, TOT, and LATE with noncompliance1 report
  9. 9.Compute Minimum Broadcast Latencies Between Cities1 report
  10. 10.Compute subtree sums with tree DFS1 report

Counted from 16 candidate interview experiences on Katkat. How we collect and count

Showing 91–97 of 97 results

  • Show role fit using past ad experienceMedium

    This question evaluates a candidate's ability to demonstrate role fit by mapping past ads engineering projects to job requirements and communicating measurable impact, assessing competencies such as ownership, cross-functional collaboration, incident debugging, and prioritization.

    NetflixSoftware EngineerBehavioral & LeadershipOnsite
  • Simulate a TTL Cache with LRU EvictionMedium

    Implement a fixed-capacity key-value cache combining per-key TTL expiration with LRU eviction and O(1) average time per operation.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Solve core probability and statistics questionsEasy

    This problem tests your understanding of the Central Limit Theorem and its application in power analysis for A/B testing and confidence interval estimation.

    NetflixData ScientistStatistics & MathOnsite
  • Solve non-repeating show substringMedium

    This question evaluates understanding of sequence processing, uniqueness detection, and the use of auxiliary data structures to track previously seen elements when finding non-repeating contiguous subsequences, applied to show names instead of characters.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Solve sliding-window and disjoint-string-pairs tasksMedium

    This question tests the ability to combine sliding window techniques with bit manipulation or frequency counting to identify pairs of substrings that share no common characters.

    NetflixSoftware EngineerCoding & AlgorithmsTechnical Screen
  • Transform flat keys into nested dictionaryMedium

    This question evaluates proficiency in parsing and restructuring hierarchical parameter keys into nested dictionary structures, string manipulation for grouping, handling nonconforming keys and duplicates, and reasoning about time and space complexity.

    NetflixMachine Learning EngineerData Manipulation (SQL/Python)Technical Screen
  • Write SQL for rolling frequency capsMedium

    This question evaluates proficiency with rolling-window analytics, frequency cap logic, time-based attribution, and per-user/per-campaign aggregation for advertising data.

    NetflixData ScientistData Manipulation (SQL/Python)Onsite

Netflix interview experiences

All 16 →
  • Phone Screen on a Key-Value Store with TTL, RejectedNetflix · Software Engineer · Senior+ · Technical Screen · Oct 2026
    Rejected

    I recently interviewed for an IC5 role on the Netflix Ads team. The phone screen was the classic Key-Value Store with TTL. Personally I felt it went okay. I implemented the usual set of methods, and for TTL expiration I used a min heap keyed on timestamp. I explained my design thinking and the tradeoffs as I went, wrote test cases at the end, and the code ran correctly. I still got rejected. The…

  • 90-Minute OA With 4 MC and 2 Coding QuestionsNetflix · Machine Learning Engineer · Intern · Online Assessment · Sep 2026

    I just finished the Netflix MLE Intern OA. It was timed at 90 minutes, with 6 questions total: 4 multiple choice and 2 coding. Below are the topics I remember. I don't fully remember the exact wording or details anymore, but I hope this helps others preparing for the same OA. The multiple choice questions were fairly basic overall. The topics I can recall: Bayes' formula; simple calculations invo…

  • Data Platform Loop, Rejected After the Final System Design RoundNetflix · Software Engineer · Onsite · Sep 2026
    Rejected

    I think the team I interviewed for was data platform. A recruiter reached out because I had related experience a few years ago. From other posts I used to think the problem solving round and the coding round were different, but after going through it they feel like essentially the same thing. Netflix's interview process is very long, and different teams probably test different things, so you need…

  • ML Fundamentals Plus a Production PR Review, RejectedNetflix · Machine Learning Engineer · Technical Screen · Sep 2026
    Rejected

    Sharing a failure story from September. The recruiter reached out to me on LinkedIn, and then we scheduled a phone screen. Since this was hiring for a newly formed applied GenAI org, the format was pretty new and I haven't seen it in any other interview write-ups: 30 min of ML fundamentals + 30 min of practical coding. 30 min ML fundamentals, three questions: What is a KV cache? What is attention…

  • Two Back-to-Back Coding Rounds and a No-Feedback RejectionNetflix · Software Engineer · Technical Screen · Aug 2026
    Rejected

    The questions were different from every interview report I had seen. There were two back-to-back interviews. First phone round — problem solving: Question: The ping times between cities are different. Starting from one city, find the fastest paths for broadcasting to every other city. Solution: Dijkstra's algorithm. Follow-up: If the ping times keep changing, how should the paths be updated? Solu…

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