Machine Learning interview questions

Showing 271–300 of 572 results

• Train vs Test Error Diagnosis, Overfitting Fixes by Model Type, and Cross-ValidationMedium

  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.

  LyftMachine Learning EngineerMachine LearningTechnical Screen
• Evaluate Noisy Data for LLM Post-TrainingMedium

  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.

  MercorMachine Learning EngineerMachine LearningTechnical Screen
• Review a Compact GPT-Style Transformer ImplementationMedium

  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.

  MercorMachine Learning EngineerMachine LearningOnsite
• Apply reinforcement learning to product decisionsMedium

  This question assesses the ability to determine the appropriate RL framework for recommendation systems, evaluate policies using offline data, and implement safety guardrails.

  MetaData ScientistMachine LearningOnsite
• Build harmful-content text classifierMedium

  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.

  MetaMachine Learning EngineerMachine LearningOnsite
• Choose and compute recommender evaluation metricsHard

  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.

  MetaData ScientistMachine LearningHR Screen
• Choose threshold under asymmetric costsMedium

  This question evaluates cost-sensitive classification, operating-threshold selection, score calibration, drift monitoring, and experiment and guardrail design competencies within the Machine Learning domain.

  MetaData ScientistMachine LearningOnsite
• Deploy multi-armed bandits safelyHard

  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.

  MetaData ScientistMachine LearningOnsite
• Derive Linear Regression SolutionMedium

  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.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Design Machine Learning Model for Facebook Groups Post RankingHard

  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.

  MetaData ScientistMachine LearningOnsite
• Design a Restaurant Recommendation System for Food AppsHard

  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.

  MetaData ScientistMachine LearningOnsite
• Design an ad recommendation and ranking systemMedium

  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.

  MetaData ScientistMachine LearningOnsite
• Design and evaluate an ads ranking algorithmEasy

  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.

  MetaAnalytics EngineerMachine LearningOnsite
• Detect leakage and evaluate a prediction modelHard

  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.

  MetaData ScientistMachine LearningOnsite
• Employ Collaborative Filtering for Personalized Recommendation ListsMedium

  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.

  MetaData ScientistMachine LearningTechnical Screen
• Evaluate Classifier with Precision, Recall, and Fairness MetricsMedium

  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.

  MetaData ScientistMachine LearningTechnical Screen
• Evaluate Factors Before Replacing Recommendation ModelHard

  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.

  MetaData ScientistMachine LearningOnsite
• Evaluate Fake-Account Classifier with Precision and Recall MetricsMedium

  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.

  MetaData ScientistMachine LearningOnsite
• How to design Shop ad rankingHard

  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.

  MetaData ScientistMachine LearningTechnical Screen
• How would you design a Shop Ads ranking algorithm?Easy

  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.

  MetaData ScientistMachine LearningTechnical Screen
• How would you predict a car’s turning intention?Medium

  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).

  MetaData ScientistMachine LearningOnsite
• Identify Algorithms for Detecting Malicious Duplicated ContentMedium

  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.

  MetaData ScientistMachine LearningTechnical Screen
• Identify Features for Fake News Detection on FacebookHard

  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.

  MetaData ScientistMachine LearningOnsite
• Implement 1NN Embeddings and Forward PassHard

  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.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Implement K-Means Clustering From ScratchMedium

  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.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Implement the Forward Pass of Multi-Head Attention From ScratchMedium

  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.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Machine Learning Fundamentals: Optimizers, Scaling Laws, and ClusteringHard

  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.

  MetaMachine Learning EngineerMachine LearningOnsite
• Masked Multi-Head Attention Forward Pass Without a FrameworkMedium

  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.

  MetaMachine Learning EngineerMachine LearningTechnical Screen
• Replace legacy ads model safelyHard

  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.

  MetaData ScientistMachine LearningOnsite
• Self-Attention: Implementation, Complexity, and Efficient VariantsHard

  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.

  MetaMachine Learning EngineerMachine LearningOnsite