Katkat

Company

Roku interview questions

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

Questions8

1 easy7 medium0 hard

Your progress0/8

Roku interview questions by the numbers

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Katkat has 8 Roku interview questions. The largest topics are System Design (25%), Analytics & Experimentation (13%) and Coding & Algorithms (13%). By difficulty: 13% easy and 88% medium. The most common round is Onsite (50%). Frequent concepts include Distributed Systems, System Design, Scalability and Algorithms.

Topics

Difficulty

  • Easy13% · 1
  • Medium88% · 7

Interview rounds

  • Onsite50% · 4
  • Technical Screen50% · 4

Roles

  • Data Scientist50% · 4
  • Backend Engineer25% · 2
  • Software Engineer25% · 2

Frequent concepts

  • Distributed Systems38% · 3
  • System Design38% · 3
  • Scalability25% · 2
  • Algorithms13% · 1
  • Bias-Variance13% · 1
  • CDN13% · 1

Showing 1–8 of 8 results

  • Compute longest common subsequence lengthMedium

    This question tests the ability to solve the Longest Common Subsequence (LCS) problem using dynamic programming to optimize overlapping subproblems.

    RokuSoftware EngineerCoding & AlgorithmsOnsite
  • Design a high-throughput ad exchangeMedium

    This problem tests your ability to design a low-latency, high-throughput distributed system capable of managing real-time bidding (RTB) auctions for streaming video advertisements.

    RokuBackend EngineerSystem DesignOnsite
  • Design a video-on-demand streaming serviceMedium

    This problem tests your ability to design a distributed video-on-demand platform, focusing on content ingestion, delivery at scale, and media processing pipelines.

    RokuSoftware EngineerSystem DesignOnsite
  • Explain Bias-Variance Trade-off Simply for StakeholdersEasy

    Evaluates the ability to explain the bias-variance trade-off to non-technical stakeholders. Strong answers define bias as underfitting, variance as overfitting, use a clear analogy, and explain why models must generalize to new data rather than memorize training data.

    RokuData ScientistStatistics & MathTechnical Screen
  • Explain and quantify bias-variance tradeoffMedium

    This question tests the conceptual understanding of the bias-variance decomposition of mean squared error and how specific modeling choices influence these components.

    RokuData ScientistMachine LearningTechnical Screen
  • Explain concurrency and distributed fundamentalsMedium

    This question tests fundamental knowledge of thread-safe collections in Java and core distributed systems architectural patterns, specifically partitioning strategies.

    RokuBackend EngineerSoftware Engineering FundamentalsOnsite
  • Measure Speaker's Impact Using Propensity Score MatchingMedium

    Evaluates causal inference for optional smart speaker ownership using propensity score matching and staggered adoption. Strong answers address self-selection, construct matched or weighted controls, validate balance and pre-trends, estimate ATT, and run placebo and sensitivity analyses.

    RokuData ScientistAnalytics & ExperimentationTechnical Screen
  • Write SQL for profit, growth, retentionMedium

    This question evaluates a candidate's ability to perform advanced SQL data manipulation — specifically rolling-window aggregations, revenue and gross profit computations that account for refunds, order status and test flags, plus cohort retention and week-over-week growth analysis.

    RokuData ScientistData Manipulation (SQL/Python)Technical Screen

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