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Machine Learning Interview Questions
Practice 9403 coding, system design and machine learning questions from 546 companies, sorted by role, topic and interview round. Every question has step-by-step hints, a code runner that supports 8 languages and an AI mentor; members also get the full solution walkthrough.
- 9403
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 271–300 of 572 results
- LyftMediumMachine Learning EngineerTrain vs Test Error Diagnosis, Overfitting Fixes by Model Type, and Cross-Validation
A machine learning fundamentals screen that asks you to diagnose models from pairs of training and test error, including the unusual case where the test error is the lower one. It continues with curbing overfitting in linear, tree-based and neural network models, and with setting up validation splits and k-fold cross-validation.
Machine LearningTechnical Screen - MercorMediumMachine Learning EngineerEvaluate Noisy Data for LLM Post-Training
Design a curation and validation framework to evaluate, filter, and safely integrate a large, noisy dataset into an LLM post-training pipeline without degrading model performance.
Machine LearningTechnical ScreenLLM Post-TrainingData CurationModel Evaluation - MercorMediumMachine Learning EngineerReview a Compact GPT-Style Transformer Implementation
Assess and debug a compact decoder-only Transformer implementation. Candidates trace tensor dimensions through causal multi-head self-attention, identify flaws in next-token loss alignment and KV-cached generation, and establish equivalence between cached and uncached forward passes.
Machine LearningOnsiteTransformersPyTorchDeep Learning - MetaMediumData ScientistApply reinforcement learning to product decisions
This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.
Machine LearningOnsiteReinforcement LearningRecommendation SystemsOffline Evaluation - MetaMediumMachine Learning EngineerBuild harmful-content text classifier
This problem evaluates your ability to design a robust text classification system for identifying harmful content, emphasizing strategies for data imbalance, labeling ambiguity, and real-time inference.
Machine LearningOnsiteNLPClassificationMLOps - MetaHardData ScientistChoose and compute recommender evaluation metrics
This question evaluates competency in offline recommender-system evaluation, propensity-weighted policy comparison, classification metric interpretation, probability calibration, model selection trade-offs, and handling data leakage and network interference.
Machine LearningHR Screen - MetaMediumData ScientistChoose threshold under asymmetric costs
This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.
Machine LearningOnsite - MetaHardData ScientistDeploy multi-armed bandits safely
This question evaluates proficiency in online experimentation and sequential decision-making, covering Bayesian bandits (Thompson Sampling), safety guardrails for churn, delayed-conversion handling, traffic allocation and stopping/rollback policy design.
Machine LearningOnsite - MetaMediumMachine Learning EngineerDerive Linear Regression Solution
This problem tests your ability to derive the Ordinary Least Squares (OLS) closed-form solution for a simple linear regression model and analyze the underlying mathematical assumptions.
Machine LearningTechnical ScreenLinear RegressionMathematicsMLE - MetaHardData ScientistDesign Machine Learning Model for Facebook Groups Post Ranking
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 Design Machine Learning Model for Facebook Groups Post Ranking states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaHardData ScientistDesign a Restaurant Recommendation System for Food Apps
Meta machine learning system-design prompt on restaurant recommendations for food apps, covering retrieval and ranking models, features, cold start, implicit feedback, offline metrics, A/B testing, and marketplace guardrails.
Machine LearningOnsite - MetaMediumData ScientistDesign an ad recommendation and ranking system
This question evaluates end-to-end ad recommendation and ranking design skills, covering machine learning modeling, recommender systems, causal and counterfactual evaluation, online learning, and production ML engineering within the Machine Learning domain, at a high-level system-design and modeling abstraction.
Machine LearningOnsite - MetaEasyAnalytics EngineerDesign and evaluate an ads ranking algorithm
This problem tests your understanding of the end-to-end machine learning lifecycle for an ads ranking system, focusing on feature engineering, model selection, and the transition from offline metrics to online business impact.
Machine LearningOnsiteAds RankingMetricsEvaluation - MetaHardData ScientistDetect leakage and evaluate a prediction model
This question evaluates competence in production-ready supervised machine learning—detecting and repairing data leakage in logs-based features, designing time-aware cross-validation, defining KPI-driven thresholding and calibration, interpreting model coefficients, and establishing monitoring and retraining policies for a churn prediction model in the Machine Learning domain. It is commonly asked because interviewers need to assess both conceptual understanding of leakage, validation, and calibration principles and practical application skills for deploying and maintaining models with cost-sensitive objectives and monitoring in production, so the level of abstraction spans conceptual reasoning and hands-on practical application.
Machine LearningOnsite - MetaMediumData ScientistEmploy Collaborative Filtering for Personalized Recommendation Lists
Meta machine learning prompt on collaborative filtering and personalized ranking, covering multi-stage recommenders, embeddings, learning-to-rank, role and context constraints, cold start, serving architecture, and evaluation.
Machine LearningTechnical Screen - MetaMediumData ScientistEvaluate Classifier with Precision, Recall, and Fairness Metrics
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 Evaluate Classifier with Precision, Recall, and Fairness Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - MetaHardData ScientistEvaluate Factors Before Replacing Recommendation Model
Evaluates whether an ads recommendation model should replace an existing production model. Strong answers combine offline validation, online experiments, revenue and advertiser outcomes, user guardrails, conflicting metric interpretation, rollout safety, rollback planning, and executive communication.
Machine LearningOnsite - MetaMediumData ScientistEvaluate Fake-Account Classifier with Precision and Recall Metrics
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 Evaluate Fake-Account Classifier with Precision and Recall Metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaHardData ScientistHow to design Shop ad ranking
This problem tests your ability to design a multi-objective ranking system for e-commerce ads, balancing short-term engagement, advertiser conversion, and platform revenue while mitigating feedback loops.
Machine LearningTechnical ScreenAd RankingMulti-task LearningRecommender Systems - MetaEasyData ScientistHow would you design a Shop Ads ranking algorithm?
This problem tests the ability to design an end-to-end ad ranking system, focusing on objective optimization, feature engineering, and mitigating common biases in marketplace environments.
Machine LearningTechnical ScreenSystem DesignRankingAds - MetaMediumData ScientistHow would you predict a car’s turning intention?
This question evaluates applied machine learning skills for vehicle intention prediction, covering temporal sequence modeling versus feature engineering, map- and lane-aware representations, multi-agent interaction reasoning, labeling/time-horizon definition, evaluation metrics and calibration, class imbalance and distribution shift handling, and uncertainty estimation for downstream planning. It is commonly asked because intention prediction is safety-critical in autonomous driving and probes a candidate’s ability to make system-level design trade-offs across data labeling, model architecture, training/validation, and risk-aware evaluation. Category/Domain: Machine Learning; Abstraction level: system-level applied ML for data scientist roles (mid-to-senior).
Machine LearningOnsite - MetaMediumData ScientistIdentify Algorithms for Detecting Malicious Duplicated Content
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 Identify Algorithms for Detecting Malicious Duplicated Content states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - MetaHardData ScientistIdentify Features for Fake News Detection on Facebook
Identify Features for Fake News Detection on Facebook evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningOnsite - MetaHardMachine Learning EngineerImplement 1NN Embeddings and Forward Pass
Implement a fully vectorized forward pass that projects input features into an embedding space and classifies them using a 1-nearest-neighbor lookup against a reference bank.
Machine LearningTechnical Screennumpydeep-learninglinear-algebra - MetaMediumMachine Learning EngineerImplement K-Means Clustering From Scratch
An ML coding question that asks for a from-scratch implementation of k-means clustering with no provided test cases. It tests the assignment and update steps, vectorized distance computation, initialization, stopping rules, empty-cluster handling and the ability to verify code with self-constructed checks.
Machine LearningTechnical Screen - MetaMediumMachine Learning EngineerImplement the Forward Pass of Multi-Head Attention From Scratch
An ML coding question that asks for the forward pass of multi-head attention from scratch. It tests query, key and value projections, splitting and merging heads, scaled dot-product attention with a stable softmax, causal and padding masks, complexity in sequence length, and self-made correctness checks.
Machine LearningTechnical Screen - MetaHardMachine Learning EngineerMachine Learning Fundamentals: Optimizers, Scaling Laws, and Clustering
Tests deep conceptual mastery of modern ML core foundations: AdamW vs. Adam weight decay mechanics, compute-optimal Chinchilla scaling laws, and the mathematical equivalence between Hard Expectation-Maximization in GMMs and k-means clustering.
Machine LearningOnsiteOptimizersScaling LawsClustering - MetaMediumMachine Learning EngineerMasked Multi-Head Attention Forward Pass Without a Framework
Implement batched multi-head self-attention by hand, including projections, head split and merge, scaled dot products, a numerically stable masked softmax, and causal and padding masks, then prove it correct with tests you design yourself. Tests shape discipline and attention fundamentals.
Machine LearningTechnical Screentransformersattentionnumerical-stability - MetaHardData ScientistReplace legacy ads model safely
This question evaluates competence in machine learning systems engineering, including offline validation, counterfactual/off‑policy evaluation, online experimentation design, and production monitoring for ad-ranking models.
Machine LearningOnsite - MetaHardMachine Learning EngineerSelf-Attention: Implementation, Complexity, and Efficient Variants
Tests a candidate's mathematical and practical grasp of scaled dot-product attention, its computational and memory bottlenecks, and modern algorithmic optimizations like FlashAttention and linear attention.
Machine LearningOnsitetransformerattention-mechanismflashattention
Lyft
Mercor
Meta