Machine Learning Interview Questions

Showing 271–300 of 348 results

• SquarepointMedium·Data Scientist
  Explain Linear Regression Assumptions, Evaluation, and Feature Addition

  Explain least-squares regression, inference assumptions, evaluation metrics, and why added features affect training and test R-squared differently.

  Machine LearningTechnical Screen
• SquarepointMedium·Quantitative Trader
  Solve Probability and Statistics Questions

  This problem tests your understanding of OLS regression assumptions, the asymptotic behavior of sample estimators, and expected value optimization under probabilistic constraints.

  Machine LearningTechnical ScreenRegressionProbabilityStatistics
• StripeHard·Data Scientist
  Design a leak-free time-split model

  This question evaluates a Data Scientist's competency in time-aware predictive modeling, including label leakage prevention for late-arriving events, temporal cross-validation, feature engineering, model selection (logistic regression vs gradient-boosted trees), calibration and thresholding for costed actions.

  Machine LearningTechnical Screen
• StripeHard·Data Scientist
  Design a target‑user prediction system

  This question evaluates skills in temporal label definition and leakage prevention, time‑bounded feature engineering (recency/frequency and content affinity), uplift and incremental effect estimation, model selection under class imbalance and budgeted outreach, offline metric design, calibration, fairness constraints, and production validation and monitoring. Common in Machine Learning/Data Science interviews, it is asked to gauge both conceptual understanding of causal and validation issues and practical application skills for deploying a ranked outreach model under capacity constraints, including appropriate cross‑validation and thresholding to map predictive scores to business outcomes.

  Machine LearningTechnical Screen
• StripeMedium·Machine Learning Engineer
  Normalize targets for multitask regression

  This question evaluates proficiency in multitask regression, preprocessing, and loss engineering within the Machine Learning domain, focusing on handling heterogeneous target distributions, sign-constrained labels, normalization, balanced loss combination, and mapping predictions back to original units at inference.

  Machine LearningTechnical Screen
• TeslaHard·Machine Learning Engineer
  Compare RNNs, LSTMs, Transformers, and MPC

  This question evaluates understanding of sequence-modeling architectures (RNNs, LSTMs, Transformers) and Model Predictive Control, assessing architectural choice, training and optimization trade-offs, handling of long-range dependencies, and methods for integrating learned dynamics with control.

  Machine LearningTechnical Screen
• TeslaEasy·Machine Learning Engineer
  Compute Conv2D parameter counts

  This question evaluates understanding of convolutional neural network parameterization, specifically how kernel dimensions, input/output channels and an optional bias term determine the number of learnable parameters.

  Machine LearningTechnical Screen
• TeslaHard·Machine Learning Engineer
  Design RL reward for speed limits

  This question evaluates reinforcement learning competencies—reward engineering for speed-constrained control, distinctions in policy optimization methods (PPO versus GRPO), and reasoning about time-varying constraints and partial observability using trajectory data.

  Machine LearningTechnical Screen
• TeslaHard·Machine Learning Engineer
  Implement attention and Transformer with backward pass

  This question evaluates implementation and analytical skills for scaled dot-product multi-head self-attention and an encoder-style Transformer block, including manual forward and backward-pass computations, gradient derivation for projection matrices, causal masking for autoregressive next-token prediction, numerical stability of softmax, and time/memory complexity analysis within the Machine Learning domain, emphasizing practical implementation with underlying conceptual understanding of linear algebra and backpropagation mechanics. It is commonly asked to verify mastery of attention mechanisms and autodiff-free gradient reasoning, plus the ability to compute cross-entropy/perplexity, perform finite-difference gradient checks, and reason about algorithmic trade-offs.

  Machine LearningTechnical Screen
• The Trade DeskEasy·Data Scientist
  Design a CVR model for RTB bidding

  This Trade Desk data-science screen asks you to design an end-to-end conversion-rate (CVR) model for real-time bidding (RTB) at a DSP. It covers RTB system roles, label/attribution-window definition, bid-time feature engineering and leakage, logistic-regression vs LightGBM tradeoffs, log-loss training, class-imbalance handling with calibration, PR-AUC vs ROC-AUC evaluation, and latency-aware, safely-deployed production.

  Machine LearningTechnical Screen
• 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 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
• 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
• 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.

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• 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