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Prep plan
Updated weekly · Last refresh Sep 22

Roku Machine Learning Engineer Interview Questions

The questions to prepare for a Roku Machine Learning Engineer interview. Questions from real interview reports rank first. Updated daily.

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~3htotal time
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1
Machine LearningStart here. 5 questions · ~44 min
Feature Engineering for Sparse DataMedium

Explain how to engineer features for high-dimensional sparse data while controlling overfitting, dimensionality, and training cost.

data preprocessingFeature Engineeringsparse datasetsRoku
Similarity Scoring for ContentHard

Design a movie and series similarity score, then validate whether it improves Roku recommendations against a production baseline.

Feature Engineeringexperimental designModel EvaluationRoku
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2
System Design5 questions · ~44 min
Design a Low Latency Inference PlatformHard

Design a low latency ML inference platform for high-frequency online predictions with strict response times and evolving model features.

high-frequency requestslatencysystem architectureRoku
Distributed Training on User LogsHard

Design a distributed pipeline that turns Roku user activity logs into reliable, scalable model training data and deployed models.

distributed trainingdistributed systemsmodel trainingRoku
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3
Behavioral & Leadership7 questions · ~62 min
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4
More topics4 questions · ~35 min
Data Quality in ETL PipelinesEasy

Approach for maintaining data quality and integrity across ETL pipelines.

IdempotencyData ModelingQualityRoku
LRU Cache ImplementationMedium
Practice

Implement an LRU cache with O(1) get and put operations using a hash map and doubly linked list.

Dynamic ProgrammingcachingAlgorithmsRoku
Precision, Recall, and AccuracyHard

Compute and validate precision, recall, and accuracy for binary predictions evaluated across multiple groups.

ClassificationMetricsPrecisionRoku
Search Relevance MetricsMedium

Tests your ability to define evaluation metrics that align with user outcomes for Roku search.

success metricsModel Evaluationsearch relevanceRoku
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