Tubi interview questions

Showing 1–11 of 11 results

• Compute daily net users from event logsMedium

  This question evaluates SQL-based data manipulation skills focused on daily aggregation, cohort and retention calculations, deduplicating event records, and grouping metrics by device_platform.

  TubiData ScientistData Manipulation (SQL/Python)Technical Screen
• Determine A/B test sample size driversHard

  This question evaluates statistical power and sample size determination for A/B testing, covering hypothesis testing for proportions, derivation using normal approximations, the impact of MDE, α and power choices, allocation ratios, cluster design (ICC and design effect), group‑sequential monitoring, and adjustment for overdispersed count outcomes.

  TubiData ScientistAnalytics & ExperimentationTechnical Screen
• Execute TTL Cache Operations with Fetch-on-MissMedium

  Implement a deterministic TTL cache simulator with put, get, delete, size, and fetch-on-miss operations. Preserve expired records until deletion or replacement, model loader success and failure explicitly, and validate timestamped JSON-compatible inputs.

  TubiSoftware EngineerCoding & AlgorithmsTechnical Screen
• Explain ML basics and recommender tuningMedium

  This question evaluates core machine learning competencies including overfitting and regularization, ensemble methods like bagging, linear and logistic regression, transformer architectures, optimizer trade-offs (SGD vs Adam), hyperparameter tuning in production, and end-to-end recommender system design and deployment.

  TubiMachine Learning EngineerMachine LearningTechnical Screen
• Handle multiple experiment metrics and communicationHard

  This question assesses the ability to design a multi-metric experiment, manage the false positive rate under multiple testing, and communicate trade-off decisions to non-technical stakeholders.

  TubiData ScientistAnalytics & ExperimentationTechnical Screen
• Implement K-Means and compare with GMMMedium

  This question evaluates understanding and practical implementation of clustering algorithms, specifically K-Means centroid initialization, assignment and update mechanics, convergence criteria and edge-case handling, along with comparative knowledge of Gaussian Mixture Models and the Expectation-Maximization training framework.

  TubiMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• ML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)Medium

  This question tests ML system design competency, specifically the ability to architect end-to-end recommendation pipelines covering retrieval, ranking, feature engineering, and low-latency serving at scale. It assesses practical knowledge of two-stage recommender systems, implicit feedback modeling, cold-start handling, and critical evaluation of AI-generated code — skills central to machine learning engineering interviews.

  TubiMachine Learning EngineerML System DesignTechnical Screen
• ML System Design: Thumbnail Selection for a Streaming CatalogMedium

  This ML system design question tests a candidate's ability to architect an end-to-end personalization and ranking system, covering problem framing, reward modeling, and low-latency serving at scale. It evaluates practical knowledge of contextual bandits, engagement-signal design, cold-start handling, and off-policy evaluation — core competencies for machine learning engineering roles.

  TubiMachine Learning EngineerML System DesignTechnical Screen
• Machine Learning Fundamentals: Tree Models, Training, Evaluation, and EmbeddingsMedium

  This question assesses foundational machine learning knowledge across multiple core domains, including tree-based models, supervised training, model evaluation, embeddings, and transformer architecture. It is commonly used in ML engineer interviews to gauge whether a candidate can explain key concepts with correct mechanics, articulate trade-offs, and reason through failure modes.

  TubiMachine Learning EngineerMachine LearningTechnical Screen
• Make Cache Fetch Thread-Safe and Single-FlightMedium

  Design a thread-safe cache fetch operation that coalesces concurrent misses for the same key while allowing different keys to load in parallel. Define linearization points, generation-aware publication, lock scope, timeout and error handling, deletion races, and stale completion cleanup.

  TubiSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Weighted Random Sampling by IndexMedium

  This question tests a candidate's ability to implement weighted random sampling using prefix sums and binary search, evaluating practical algorithm design for probability distributions. It assesses proficiency in translating mathematical probability constraints into efficient data structures, a skill commonly probed in machine learning engineering roles where non-uniform sampling is fundamental to model training and inference pipelines.

  TubiMachine Learning EngineerCoding & AlgorithmsTechnical Screen