TikTok interview questions

Showing 31–60 of 176 results

• Count subarrays summing to targetMedium

  This problem tests your ability to optimize subarray sum calculations from an O(n^2) approach to O(n) using a hash map for prefix sum tracking.

  TikTokMachine Learning EngineerCoding & AlgorithmsTechnical Screen
• Debug a Hive Query for DAUMedium

  This question evaluates proficiency in Hive/SQL query formulation, data partitioning and pruning, join and aggregation semantics, timestamp handling, and performance tuning for large-scale analytics.

  TikTokData EngineerData Manipulation (SQL/Python)Technical Screen
• Debug a Hive insert queryMedium

  This question evaluates a candidate's ability to debug Hive INSERT/SELECT operations, focusing on schema and column alignment, data types and casts, partition handling, file formats/SerDe, ACID/transactional constraints, and syntax or reserved keyword issues.

  TikTokData EngineerData Manipulation (SQL/Python)Technical Screen
• Define QKV for recommender cross-attentionHard

  This question evaluates understanding of Transformer-style cross-attention and the concrete design of Query, Key, and Value tensors for deep-learning recommender systems, testing representation semantics, embedding alignment, and interaction modeling between user history, candidate items, and context.

  TikTokMachine Learning EngineerMachine LearningTechnical Screen
• Delete nodes in linked list and binary treeMedium

  This question evaluates proficiency in mutable data structure operations, specifically linked list node removal and binary tree node replacement/deletion, requiring knowledge of traversal, pointer/reference manipulation, and edge-case handling.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Demonstrate leadership in cross-functional disagreementMedium

  This question evaluates leadership, cross-functional collaboration, stakeholder management, experimental design, and metric-driven decision-making competencies in a data scientist context, with emphasis on trade-off reasoning and end-to-end execution.

  TikTokData ScientistBehavioral & LeadershipHR Screen
• Derive L1 vs L2 effects with correlationMedium

  This question evaluates understanding of regularization methods (ridge/L2, LASSO/L1, and elastic net), multicollinearity effects, geometric intuition of constraint sets, and hyperparameter influence on coefficient patterns in the Statistics & Math domain.

  TikTokData ScientistStatistics & MathTechnical Screen
• Describe Your Most Impactful Project Experience and Lessons LearnedMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe Your Most Impactful Project Experience and Lessons Learned states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokData ScientistBehavioral & LeadershipTechnical Screen
• Describe a data-heavy projectMedium

  Describe a data-heavy project evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokData EngineerBehavioral & LeadershipTechnical Screen
• Describe a project you are proud ofMedium

  This question evaluates technical leadership, ownership, system design, execution, collaboration, trade-off reasoning, and the ability to demonstrate measurable impact from a software project.

  TikTokSoftware EngineerBehavioral & LeadershipTechnical Screen
• Describe a service performance challengeMedium

  This question evaluates competency in production performance engineering, observability-driven diagnosis, experimentation and impact quantification, as well as leadership in prioritizing trade-offs and risk mitigation.

  TikTokSoftware EngineerBehavioral & LeadershipTechnical Screen
• Describe career plan and teamwork approachMedium

  This question evaluates a candidate's career planning, self-awareness, and teamwork competencies, specifically goal setting, alignment with team priorities, conflict resolution, and collaborative decision-making.

  TikTokSoftware EngineerBehavioral & LeadershipTechnical Screen
• Describe internship and research projectsMedium

  This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Describe internship and research projects states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Describe toughest challenge and resolutionMedium

  This question evaluates a candidate's problem-solving, leadership, communication, decision-making, and ability to quantify impact when resolving complex technical or organizational challenges.

  TikTokSoftware EngineerBehavioral & LeadershipTechnical Screen
• Design A/B Test for Cost-Per-Conversion Efficiency AnalysisHard

  Design A/B Test for Cost-Per-Conversion Efficiency Analysis evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokData ScientistAnalytics & ExperimentationOnsite
• Design LRU cache with O(1) operationsMedium

  This question evaluates a candidate's understanding of data structures and algorithm design, specifically skills in implementing constant-time cache operations, eviction policies, update handling, and concurrency/thread-safety considerations.

  TikTokSoftware EngineerCoding & AlgorithmsTechnical Screen
• Design Metrics for Content Moderation and Chatbot EvaluationMedium

  This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design Metrics for Content Moderation and Chatbot Evaluation states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  TikTokData ScientistAnalytics & ExperimentationTechnical Screen
• Design Real-Time Credit Card Fraud Detection SystemHard

  Evaluates real-time credit-card fraud detection system design under latency, drift, and asymmetric costs. Strong answers cover data, delayed labels, features, models, serving architecture, monitoring, retraining, and thresholding.

  TikTokData ScientistMachine LearningOnsite
• Design a Warehouse for Key MetricsHard

  This question evaluates a data engineer's skills in dimensional modeling, data warehousing, and analytics infrastructure, covering facts and dimensions, grain definition, surrogate keys, partitioning and clustering strategies, and operational concerns like deduplication, late-arriving events, null/anonymous users, and slowly changing dimensions.

  TikTokData EngineerSystem DesignTechnical Screen
• Design a creator posting-frequency experimentMedium

  This problem tests the ability to design a controlled experiment while accounting for network interference, non-compliance, and selection bias in a two-sided platform.

  TikTokData ScientistAnalytics & ExperimentationOnsite
• Design a distributed key-value storeMedium

  This problem tests your ability to design a scalable, fault-tolerant distributed key-value store, focusing on partitioning strategies, replication, and handling node failures.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design a high-concurrency ticketing systemHard

  TikTok software-engineer system-design interview question: design a high-concurrency ticketing / flash-sale system that survives millions of concurrent purchase attempts with zero oversell. It tests admission control and fairness (virtual waiting room, FIFO vs lottery), atomic inventory allocation with TTL reservations, anti-bot defenses, idempotent order creation, hot-key sharding, the data model and APIs, caching, message queues and delivery semantics, consistency and reconciliation, failure handling, capacity planning, SLOs, load testing, and monitoring.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design a model to choose dynamic KMedium

  This question evaluates a candidate's competency in ML system design and decision-focused modeling for recommender pipelines, focusing on retrieval-ranking trade-offs, latency and compute cost constraints, and dynamic per-request selection of K.

  TikTokMachine Learning EngineerML System DesignOnsite
• Design an ad-selection system across objectivesHard

  This question evaluates expertise in machine learning system design for ad selection and real-time bidding, covering predictive modeling and calibration, value unification across objectives, auction mechanics, budget pacing, exploration strategies, guardrails, and evaluation methodologies.

  TikTokData ScientistMachine LearningTechnical Screen
• Design an experiment for exploratory recommendationsHard

  Evaluates experiment design for exploratory recommendation algorithms that may trade short-term engagement for discovery. Strong answers define exploration, satisfaction, retention, ecosystem health, leading indicators, and cautious launch decisions.

  TikTokData ScientistAnalytics & ExperimentationTechnical Screen
• Design an interference-robust A/B test for monetizationHard

  This question evaluates experimental design and causal inference skills within Analytics & Experimentation, emphasizing interference mitigation, clustering and randomization choices, eligibility and exposure rules, metric hierarchy and guardrails, power and duration planning, variance-reduction techniques, and cluster-robust inference in a two-sided marketplace. It is commonly asked to test a data scientist's practical-application ability to balance monetization and growth trade-offs through precise statistical planning, pre-registered stopping rules, SRM detection, MDE and rollout decision thresholds rather than purely conceptual understanding.

  TikTokData ScientistAnalytics & ExperimentationHR Screen
• Design and explain your project architectureHard

  A TikTok software-engineer system-design screen that asks you to walk through one of your recent projects end-to-end: architecture diagram, components and data stores, sync vs. async data flows and protocols, scaling and capacity planning, fault tolerance, backpressure, idempotency, observability, and security. It also probes the design trade-offs you made, the real failure modes you hit, the measurable impact, and what you would redesign. Includes a fully worked reference answer plus the scoring rubric.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design automated regression tests for an APIEasy

  This question assesses the ability to structure a comprehensive automated regression suite for a RESTful API, focusing on test case selection, validation strategies, and maintenance.

  TikTokSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Design low-latency large-scale hotel booking systemMedium

  This question evaluates understanding of large-scale backend and distributed system design, including scalability, high availability, consistency models, concurrency control to prevent double-booking, hotspot mitigation, and protocol trade-offs for low-latency booking and availability flows.

  TikTokSoftware EngineerSystem DesignTechnical Screen
• Design multimodal deployment under compute limitsEasy

  This question evaluates competency in Machine Learning systems engineering for a Data Scientist role, covering multimodal model deployment and inference optimization, scalable retrieval (ANN and hybrid search), generalization and regularization concepts (overfitting, dropout), normalization methods, and RLHF, emphasizing both theoretical principles and engineering feasibility. It is commonly asked to assess reasoning about trade-offs between quality, latency, and cost in resource-constrained environments, validation via offline/online metrics, and the ability to bridge conceptual understanding with practical deployment and system-level design considerations.

  TikTokData ScientistMachine LearningOnsite