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Machine Learning Engineer 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
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- 546
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- 11
- Topics
- 8
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Showing 301–330 of 782 results
- MercorMediumMachine Learning EngineerDesign a DAG Pipeline Runner for Data-Labeling Jobs
Design a multi-tenant service that runs user-defined DAGs of automated steps (pre-processing, model pre-labeling, QA checks, exports), tracks every run, and survives crashes. Tests data modeling, separating the scheduler from workers, durable state, retries and idempotency, tenant fairness and observability.
System DesignOnsiteworkflow-orchestrationdistributed-systemsstate-machines - MercorMediumMachine Learning EngineerDesign a High-Demand Event Ticket Marketplace
Design an event ticketing platform capable of handling flash-sale spikes without overselling, featuring transactional seat holds, idempotent payment processing, and virtual queueing.
System DesignOnsitesystem-designdistributed-systemsconcurrency - MercorEasyMachine Learning EngineerDesign a Social Feed
Design an end-to-end personalized social feed system capable of serving low-latency ranked content while handling hybrid fanout, multi-stage ML candidate retrieval, and stable pagination.
System DesignTechnical Screensystem-designfeed-rankingrecommendation-systems - MercorMediumMachine Learning EngineerDesign a workflow orchestration system that runs multi-step jobs reliably
A system design question asking you to build a workflow system where users define multi-step workflows with dependencies, start runs, and track progress. It tests data modeling, orchestration and worker architecture, durable state, retries and idempotency, fairness between tenants, scaling and observability.
System DesignOnsite - 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 EngineerImplement a Referral Network
Design and implement an in-memory referral network as a directed acyclic graph (DAG) supporting cycle-safe edge insertion, depth-bounded reachability queries, and expected reward computation under geometric decay.
Coding & AlgorithmsOnsiteGraph AlgorithmsDAGBFS - 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 - MetaMediumMachine Learning EngineerAnswer behavioral questions on projects and feedback
This set of behavioral questions evaluates communication, leadership, adaptability, handling ambiguity, feedback exchange, and project ownership within the context of machine learning engineering projects.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerAnswer core behavioral questions using STAR
This question evaluates behavioral and leadership competencies—including communication, teamwork, conflict resolution, time and scope management, and mentorship—within the context of a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - MetaMediumMachine Learning EngineerAnswer senior-level behavioral interview questions
This question evaluates leadership, decision-making, risk management, stakeholder management, and impact-communication skills for a senior Machine Learning Engineer role within the Behavioral & Leadership category.
Behavioral & LeadershipTechnical Screen - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
ML System DesignOnsite - MetaMediumMachine Learning EngineerBuild Friend Recommendations
This problem tests your ability to manipulate graph-like data structures to perform neighbor analysis and efficient set operations to rank potential connections.
Coding & AlgorithmsTechnical ScreenGraphsHash MapsSocial Network Analysis - MetaHardMachine Learning EngineerBuild a Friend Recommender
This problem tests your ability to implement a graph-based recommendation algorithm using common neighbors and popularity constraints to rank potential connections.
Coding & AlgorithmsOnsiteGraphSortingRecommendation System - 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 - MetaMediumMachine Learning EngineerCheck diagonal and compute window statistics
This question evaluates understanding of array and matrix manipulation, sliding-window and streaming statistics, and appropriate data-structure choices for maintaining aggregates and order-statistics such as moving averages and sliding-window medians.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerCheck diagonal equality in a matrix
This question evaluates skill in matrix manipulation and algorithmic reasoning, specifically understanding diagonal traversal, equality checks, and handling boundary conditions such as empty matrices, single-row or single-column inputs, and negative values.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerCompute sliding-window medians
This question evaluates proficiency in designing and analyzing efficient sliding-window algorithms and data structures for computing running medians, including correct handling of duplicates, deletions, and the convention for defining the median when k is even.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerCompute the next lexicographic permutation
This problem tests your ability to manipulate array elements in-place to find the next lexicographically ordered sequence, focusing on understanding the properties of permutations.
Coding & AlgorithmsOnsiteArraysPermutationsTwo Pointers - MetaMediumMachine Learning EngineerDebug and optimize a card-drawing strategy
This problem tests your ability to simulate card-drawing probabilities, debug flawed logic in iterative selection strategies, and optimize performance for repeated simulations.
Coding & AlgorithmsOnsiteAlgorithmsSimulationProbability - MetaMediumMachine Learning EngineerDeep copy a linked list with random pointers
This problem tests your ability to manipulate complex pointer structures and maintain object identity across a deep copy operation in a non-linear linked list.
Coding & AlgorithmsTechnical ScreenLinked ListHash TablePointers - MetaHardMachine Learning EngineerDemonstrate leadership and ensure data compliance
This question evaluates leadership, program execution, stakeholder management, risk and tradeoff reasoning in machine learning engineering, along with expertise in data privacy, regulatory compliance, and operational data governance for user-data-driven systems.
Behavioral & LeadershipOnsite - 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 - MetaMediumMachine Learning EngineerDescribe handling intense time pressure
This question evaluates a candidate's ability to perform under intense time pressure and multitask, specifically assessing prioritization, trade-off communication, stakeholder management, stress resilience, and execution skills relevant to machine learning engineering within a Behavioral & Leadership context.
Behavioral & LeadershipOnsite - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Large-Scale Personalized Recommendation System End to End
An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and Rewards
An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.
ML System DesignTechnical Screen - MetaMediumMachine Learning EngineerDesign a Short-Video Recommendation System
This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityRanking - MetaMediumMachine Learning EngineerDesign a newsfeed dislike model
This problem tests the ability to design an end-to-end ML system for predicting user dissatisfaction, focusing on label definition, feature engineering, and the integration of negative signals into a ranking pipeline.
ML System DesignOnsiteRecommender SystemsRankingImbalanced Learning - MetaHardMachine Learning EngineerDesign a recommendation system
This question evaluates competence in designing large-scale recommendation systems and ML engineering skills such as candidate generation, feature store design (offline/online and point-in-time correctness), real-time signal ingestion, ranking and re-ranking, online exploration, cold-start strategies, feedback-bias mitigation, experimentation, and operational reliability. It is commonly asked to assess the ability to make architecture-level trade-offs for scalability, latency and throughput targets, multi-objective optimization, data governance and outage fallback behavior; it falls under the System Design and Machine Learning domain and requires practical, architecture-level application with conceptual trade-off reasoning rather than low-level implementation detail.
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