Machine Learning Interview Questions

Showing 481–510 of 572 results

• TikTokMedium·Data Scientist
  Compare Random Forests and Boosted Trees: Bias, Variance, Speed

  Evaluates practical trade-offs between Random Forests and gradient-boosted trees for tabular ML. Strong answers compare bias, variance, speed, interpretability, overfitting, production fit, and feature scaling needs.

  Machine LearningTechnical Screen
• TikTokHard·Machine Learning Engineer
  Define QKV for recommender cross-attention

  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.

  Machine LearningTechnical Screen
• TikTokHard·Data Scientist
  Design Real-Time Credit Card Fraud Detection System

  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.

  Machine LearningOnsite
• TikTokHard·Data Scientist
  Design an ad-selection system across objectives

  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.

  Machine LearningTechnical Screen
• TikTokEasy·Data Scientist
  Design multimodal deployment under compute limits

  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.

  Machine LearningOnsite
• TikTokHard·Data Scientist
  Design recommendations objective balancing growth and monetization

  This question evaluates a data scientist's competency in designing multi-objective recommender systems that balance user growth and creator monetization, covering ranking objective formulation, per-impression value estimation, constraint enforcement (latency, diversity, fairness/exposure), offline-to-online metric validation, cold-start and exploration strategies, and interference/attribution challenges within the Machine Learning domain. It is commonly asked to assess the ability to reason about trade-offs between business and user metrics, to define measurable objectives and validation plans, and to demonstrate both conceptual understanding and practical application of system design and evaluation.

  Machine LearningHR Screen
• TikTokHard·Data Scientist
  Estimate heterogeneous treatment effects with causal ML

  This question evaluates a candidate's competency in causal machine learning, heterogeneous treatment effect estimation, off-policy evaluation, and fairness-aware policy design using observational data.

  Machine LearningTechnical Screen
• TikTokMedium·Software Engineer
  Explain DPO and construct its training data

  This question evaluates understanding of Direct Preference Optimization (DPO) for fine-tuning large language models, assessing conceptual differences from PPO-based RLHF and the competency to design pairwise preference training datasets.

  Machine LearningTechnical Screen
• TikTokMedium·Machine Learning Engineer
  Explain FlashAttention, KV cache, and RoPE

  This problem tests your understanding of core transformer efficiency techniques, specifically focusing on how they improve memory footprint, latency, and sequence length handling.

  Machine LearningTechnical ScreenTransformersAttentionInference Optimization
• TikTokHard·Data Scientist
  Explain and tune XGBoost; prevent overfitting

  This question evaluates understanding of XGBoost tree-boosting internals, hyperparameter impacts on bias/variance/training time, strategies for imbalanced binary classification and leakage-aware validation, post-training calibration, and interpretability for tabular fraud detection.

  Machine LearningTechnical Screen
• TikTokMedium·Machine Learning Engineer
  Explain your VLM project end-to-end

  This question evaluates proficiency in vision-language model engineering, including model architecture (vision encoder, language model, fusion), data curation and distribution, training recipes, evaluation metrics, inference latency, and limitations analysis within the Machine Learning domain of multimodal/Vision-Language models.

  Machine LearningTechnical Screen
• TikTokEasy·Data Scientist
  How do you choose a classification threshold?

  This question evaluates a data scientist's competency in binary classification threshold selection, end-to-end ML pipeline design, model evaluation, and operational trade-offs including class imbalance, asymmetric error costs, and limited escalation capacity, within the Machine Learning domain of model evaluation and deployment.

  Machine LearningTechnical Screen
• TikTokEasy·Data Scientist
  When prioritize precision vs recall

  This question evaluates a data scientist's competency with binary classification metrics (precision and recall), cost-sensitive decision-making, and operating-threshold selection for productized models in the Machine Learning domain.

  Machine LearningTechnical Screen
• TTowerHard·Data Scientist
  Explain Why Constant Neural-Network Initialization Fails

  This question tests the conceptual understanding of weight initialization strategies and the symmetry-breaking property required for neural network gradient-based optimization.

  Machine LearningTechnical ScreenDeep LearningInitializationOptimization
• TTowerMedium·Machine Learning Engineer
  Explain the KV Cache and Prefill-Decode Disaggregation in LLM Serving

  Explain how the KV cache speeds up autoregressive decoding in transformer language models, estimate how much GPU memory it needs, and describe prefill-decode disaggregation in LLM serving. It tests understanding of compute-bound versus memory-bound inference phases, cache sizing and the trade-offs of splitting the phases across GPU pools.

  Machine LearningTechnical Screen
• TubiMedium·Machine Learning Engineer
  Explain ML basics and recommender tuning

  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.

  Machine LearningTechnical Screen
• TubiMedium·Machine Learning Engineer
  Machine Learning Fundamentals: Tree Models, Training, Evaluation, and Embeddings

  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.

  Machine LearningTechnical Screen
• TTudorMedium·Data Scientist
  Estimate Volatility from Gaussian and Brownian Samples

  This question evaluates skills in statistical estimation and stochastic process modeling, specifically parameter estimation for Gaussian samples and volatility estimation from discrete Brownian motion observations, focusing on estimating the volatility parameter (sigma) or its variance (sigma^2).

  Machine LearningTechnical Screen
• TuroHard·Data Scientist
  Compare XGBoost and LightGBM

  This question evaluates a data scientist's understanding of gradient-boosted tree implementations and practical model trade-offs, covering competencies in algorithmic differences, performance and memory characteristics, handling of missing and categorical features, regularization risks, and hyperparameter considerations for production models.

  Machine LearningOnsite
• TwitchHard·Data Scientist
  Design real-time live-stream recommendations

  This question evaluates competency in designing real-time recommender systems within the Machine Learning domain, covering objectives and label construction for streaming events, feature engineering across user/creator/stream/context, model and loss choices, bias mitigation, evaluation metrics, and low-latency serving and freshness constraints.

  Machine LearningTechnical Screen
• Two SigmaMedium·Data Scientist
  Analyze Temperatures and Update Regression

  This question evaluates proficiency in time-series and statistical analysis (defining volatility and similarity metrics), supervised regression modeling with mean squared error evaluation, greedy feature selection, and streaming/no-intercept linear regression updates.

  Machine LearningOnline Assessment
• Two SigmaHard·Data Scientist
  Build a Housing Price Prediction Model from Historical Data

  This question evaluates a data scientist's competency in end-to-end supervised machine learning for real-estate pricing, focusing on precise outcome definition, feature engineering, handling missing and time-varying data, and robustness to market regime changes.

  Machine LearningOnsite
• Two SigmaMedium·Quantitative Researcher
  Daily Temperatures by Town: Linear Models to Predict NYC Temperature

  This question evaluates proficiency in ordinary least squares linear regression (with intercept), basic statistical summaries such as standard deviation and median, and model-evaluation metrics like in-sample mean squared error applied to aligned daily time-series temperature data.

  Machine LearningTechnical Screen
• Two SigmaHard·Data Scientist
  Derive correlation bounds and omitted-variable bias

  This problem tests your understanding of the constraints on correlation matrices and your ability to analytically derive the bias introduced in linear models when a relevant predictor is ignored.

  Machine LearningTechnical ScreenLinear RegressionLinear AlgebraStatistics
• Two SigmaEasy·Data Scientist
  Design a house-price prediction model

  This problem evaluates your ability to design a robust end-to-end machine learning pipeline for house price prediction, focusing on data preprocessing, feature engineering, and validation strategy.

  Machine LearningTechnical ScreenRegressionFeature EngineeringModel Validation
• Two SigmaMedium·Data Scientist
  Design features for house price prediction

  This question tests your ability to engineer features from heterogeneous tabular data, handle missing values, and select appropriate evaluation metrics for a high-stakes regression problem.

  Machine LearningTechnical ScreenFeature EngineeringRegressionData Preprocessing
• Two SigmaHard·Data Scientist
  Forecast bikes available at a station

  This question tests the ability to design a robust time-series forecasting pipeline, specifically addressing feature engineering for cyclic temporal patterns and the selection of appropriate validation strategies for sequential data.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringForecasting
• Two SigmaMedium·Data Scientist
  How detect duplicate card records?

  This question evaluates a candidate's competency in duplicate detection, record linkage, entity resolution, and data quality assessment within credit card transaction datasets.

  Machine LearningTechnical Screen
• Two SigmaEasy·Data Scientist
  How to forecast bike dock demand

  This problem tests your ability to design a predictive pipeline for spatio-temporal demand, focusing on feature extraction from time-series data and robust validation strategies.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringValidation
• Two SigmaHard·Data Scientist
  How would you forecast bike demand?

  This problem tests your ability to design a robust machine learning pipeline for time-series demand forecasting, focusing on feature engineering for exogenous variables and evaluation metrics for spatial-temporal data.

  Machine LearningTechnical ScreenTime-SeriesFeature EngineeringRegression