Machine Learning interview questions

Showing 421–450 of 572 results

• Implement GLM Training and Prediction from an Explicit Family and LinkHard

  Explain a from-scratch generalized linear model through family and link selection, consistent loss and gradients, numerical stability, and prediction semantics.

  Quantbot TechnologiesData ScientistMachine LearningOnline Assessment
• Compare bagging, boosting, random forests, and bias-varianceHard

  This question tests the theoretical understanding of ensemble learning mechanisms and their impact on the bias-variance decomposition of prediction error.

  Qube Research & TechnologiesSoftware EngineerMachine LearningTechnical Screen
• Implement and Tune KNN ClassifierHard

  This question evaluates a candidate's competency in implementing and tuning the K-nearest neighbors algorithm, covering data preprocessing, distance-based classification, model evaluation, and hyperparameter selection.

  Qube Research & TechnologiesData ScientistMachine LearningOnline Assessment
• Analyze CTR Data and Train ModelMedium

  This problem tests the end-to-end workflow of building a CTR prediction model, focusing on feature engineering for high-cardinality categorical data and handling temporal leakage.

  RedditMachine Learning EngineerMachine LearningOnsite
• Build and evaluate click prediction modelsMedium

  This question evaluates skills in supervised probabilistic modeling for click-through rate prediction, including model selection, calibration, evaluation metric choice, cross-validation, hyperparameter tuning, and production concerns like serving and monitoring.

  RedditMachine Learning EngineerMachine LearningTechnical Screen
• Explain and Reduce Position Bias in RankingHard

  Assess understanding of position bias in click-based ranking systems, including formalizing the examination hypothesis, estimating observation propensities, training with Inverse Propensity Scoring (IPS), and designing counterfactual evaluation metrics.

  RedditMachine Learning EngineerMachine LearningTechnical Screen
• Model y from x and interpret distributionsMedium

  This question evaluates modeling judgment in machine learning, covering problem framing (regression versus classification), baseline model and metric selection, interpretation of feature or class distribution differences, and cold-start strategies for users, items, and regions.

  RedditMachine Learning EngineerMachine LearningOnsite
• Discuss overfitting, contrastive learning, transformersEasy

  This question evaluates understanding of supervised learning generalization (overfitting and underfitting), contrastive representation learning and contrastive loss functions, and transformer architectures, assessing core competencies in model evaluation, representation learning, and neural architecture concepts within the Machine Learning domain.

  ReutersSoftware EngineerMachine LearningTechnical Screen
• Find minimum of unknown convex functionMedium

  This question evaluates a candidate's understanding of convex optimization and zeroth-order (query-based) algorithm design, including reasoning about sample complexity, interval-reduction strategies, and robustness to noisy function evaluations.

  RipplingSoftware EngineerMachine LearningTechnical Screen
• Debug an inaccurate patient-message triage classifier service and write up fixesMedium

  A one-hour take-home for a senior applied AI engineering role: debug a telehealth service that sorts patient messages into care categories with an urgency and a confidence score, decide when to call an LLM or send a message to manual review, and write up bugs, priorities and trade-offs. It tests debugging, safety judgment and ML evaluation.

  Ro.CoMachine Learning EngineerMachine LearningOnline Assessment
• Design leakage-free predictive maintenance pipelineHard

  This question evaluates a data scientist's competency in designing time-series predictive maintenance pipelines, focusing on temporal feature engineering, leakage prevention and point-in-time joins, handling late-arriving labels, class imbalance and cost-sensitive thresholding, probabilistic calibration, explainability, and operational drift monitoring. It is commonly asked in the Machine Learning domain to assess an applicant's ability to produce an end-to-end, production-ready workflow that balances practical implementation concerns with conceptual system-design reasoning, so the task is primarily practical application with important conceptual elements.

  RobloxData ScientistMachine LearningOnline Assessment
• Fit logistic regression and return top featuresHard

  This question evaluates understanding of binary logistic regression, coefficient-based feature importance, and practical model-fitting considerations such as intercept inclusion and input shape handling.

  RobloxData ScientistMachine LearningOnline Assessment
• Explain and quantify bias-variance tradeoffMedium

  This question tests the conceptual understanding of the bias-variance decomposition of mean squared error and how specific modeling choices influence these components.

  RokuData ScientistMachine LearningTechnical Screen
• Implement n-gram model and select nHard

  This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Implement n-gram model and select n states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  RunwayMachine Learning EngineerMachine LearningTechnical Screen
• Explain ML and LLM fundamentalsMedium

  This question evaluates understanding of core machine learning and LLM competencies including evaluation metrics (F1 vs accuracy), differences between classification and regression, fair benchmarking, inference pipeline bottlenecks and optimization, productionization of AI services, and modern LLM concepts such as the Transformer architecture, context engineering, retrieval-augmented generation, grounding, and guardrails. It is commonly asked in Machine Learning interviews to assess both conceptual understanding and practical application, probing the candidate's ability to reason about evaluation trade-offs, system-level performance constraints, and deployment considerations rather than only algorithmic detail.

  SalesforceMachine Learning EngineerMachine LearningOnsite
• Present and Defend Recent Research on AI AgentsMedium

  This question assesses the candidate's ability to communicate complex research, isolate their specific technical contributions, and synthesize academic concepts with practical AI-agent architectural challenges.

  SalesforceSoftware EngineerMachine LearningTechnical Screen
• Explain LLM training and evaluationMedium

  This question evaluates an engineer's competency in LLM engineering, including transformer pretraining and finetuning, alignment approaches like instruction tuning and RLHF, strategies for reducing hallucinations, evaluation and production monitoring practices, and inference optimizations.

  SamsaraMachine Learning EngineerMachine LearningOnsite
• Explain LLM post-training methods and tradeoffsEasy

  This question assesses the candidate's understanding of the LLM post-training pipeline, specifically how to transition a foundation model into a helpful, safe assistant through various alignment techniques.

  Scale AIMachine Learning EngineerMachine LearningOnsite
• Explain Transformers, attention, decoding, RL, and evaluationHard

  This question evaluates understanding of transformer architectures, self- and cross-attention mechanisms, autoregressive decoding and sampling strategies, reinforcement learning–based fine-tuning (RLHF), evaluation methodologies, and training/inference optimization techniques.

  Scale AIMachine Learning EngineerMachine LearningTechnical Screen
• Implement universal adversarial attack on GPT-2Medium

  Machine learning robustness prompt on safely evaluating universal adversarial prompts for GPT-2, covering threat modeling, differentiable optimization concepts, transferability, metrics, safety controls, and mitigation strategies without deployment-ready jailbreak details.

  Scale AIMachine Learning EngineerMachine LearningTechnical Screen
• Represent k-means as an MLPEasy

  This question tests the conceptual mapping between the distance-based assignment step of k-means clustering and a single-layer neural network architecture.

  SealthMachine Learning EngineerMachine LearningOnsite
• Build a fraud detection modelMedium

  Design an end-to-end machine learning system for real-time transaction fraud detection, covering label formulation under delayed feedback, feature design, threshold selection, and continuous monitoring.

  ShopifyMachine Learning EngineerMachine LearningTechnical Screen
• Build model to predict package delivery timeMedium

  This question evaluates a candidate's competence in end-to-end machine learning system design for time-to-event forecasting, covering prediction target definition and timing, feature engineering and model selection, handling missing or delayed signals and data leakage, uncertainty quantification, and production monitoring and retraining.

  ShopifyMachine Learning EngineerMachine LearningOnsite
• Model Product RankingMedium

  This question evaluates understanding of end-to-end machine learning ranking systems, including label definition, feature engineering, model selection, training strategies, and offline/online evaluation.

  ShopifyMachine Learning EngineerMachine LearningOnsite
• Compare GRPO and PPO and Explain Sparse Versus Dense RewardsMedium

  This question evaluates your understanding of modern reinforcement learning post-training techniques for language models, focusing on GRPO versus standard PPO and the trade-offs between sparse outcome rewards and dense process supervision.

  SiemensMachine Learning EngineerMachine LearningTechnical Screen
• Explain SFT, DPO, PPO, and the Role of KL RegularizationMedium

  Compare SFT and preference optimization, explain the DPO log-ratio objective, and clarify its relationship to PPO and KL regularization.

  SiemensMachine Learning EngineerMachine LearningTechnical Screen
• Compare Batch Normalization and Layer NormalizationHard

  Compare batch and layer normalization by their axes, learned parameters, running statistics, batch-size effects, and training-to-inference debugging.

  SnapchatMachine Learning EngineerMachine LearningOnsite
• Compare convolutions and transformersMedium

  This question evaluates understanding of neural network architectures, focusing on inductive biases, data and compute trade-offs, mechanisms for handling long-range dependencies, domain-specific roles in vision and NLP, and modern hybrid approaches that address architecture limitations.

  SnapchatMachine Learning EngineerMachine LearningTechnical Screen
• Explain BatchNorm, optimizers, and L1/L2Medium

  This question tests your conceptual understanding of how normalization, optimization strategies, and regularization techniques influence training stability, convergence, and model sparsity.

  SnapchatMachine Learning EngineerMachine LearningOnsite
• Explain CLIP, contrastive losses, and retrieval limitsMedium

  This question evaluates understanding of multi-modal representation learning and retrieval systems, covering CLIP-style joint image–text encoders, contrastive loss families, embedding-based retrieval limitations, alternative retrieval paradigms, and issues like popularity bias.

  SnapchatMachine Learning EngineerMachine LearningTechnical Screen