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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.
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Showing 151–180 of 351 results
- 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.
System DesignOnsite - MetaHardMachine Learning EngineerDesign a scalable MoE pretraining pipeline
This question evaluates expertise in large-scale ML system design and distributed training for Mixture-of-Experts (MoE) models, covering competencies in model architecture, parallelism and communication strategies, memory and optimizer sharding, data curation and tokenization, monitoring, checkpointing, and fault tolerance; it is categorized under ML System Design and targets practical application with systems-level architectural reasoning rather than purely theoretical concepts. It is commonly asked to evaluate the ability to balance throughput, stability, and cost when scaling pretraining to hundreds of GPUs, reason about routing and capacity trade-offs, and design production-grade pipelines that can extend to larger clusters.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a weapon-sale ad detection system
This question tests the ability to architect an end-to-end multimodal classification pipeline for detecting policy-violating content at scale while balancing precision, recall, and operational latency.
ML System DesignOnsiteMultimodal LearningContent ModerationClassification - MetaHardMachine Learning EngineerDesign an LLM-Based Coding Assistant
Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.
ML System DesignOnsiteLLMInference ServingCode Generation - MetaHardMachine Learning EngineerDesign an LLM-Based Conversational Assistant (Chatbot)
Design an end-to-end conversational AI assistant powered by large language models, covering post-training alignment, retrieval/tool orchestration, low-latency serving, and safety guardrails at consumer scale.
ML System DesignOnsiteLLMRAGModel Serving - MetaMediumMachine Learning EngineerDesign an image copyright-violation detection system
This problem evaluates your ability to design a scalable, end-to-end computer vision pipeline capable of identifying copyrighted content within user-uploaded images, including scenarios involving cropping, filters, and collages.
ML System DesignOnsiteComputer VisionEmbeddingsSimilarity Search - MetaMediumMachine Learning EngineerDesign comment ranking for a news feed
This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.
ML System DesignOnsiteRankingRecommendation SystemsMulti-task Learning - MetaHardMachine Learning EngineerDesign nearby place recommendations
This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.
ML System DesignOnsite - MetaHardMachine Learning EngineerDesign scalable media storage and delivery
This question evaluates an engineer's ability to design globally distributed, highly scalable media storage and delivery systems, covering competencies in object storage and metadata modeling, CDN integration, replication and consistency strategies, security and access control, background processing, observability, quotas, and cost/performance trade-offs. It is commonly asked in system design interviews to assess reasoning about durability, availability, latency and cost trade-offs while requiring both high-level architectural thinking and practical implementation details; category: System Design; level of abstraction: combines conceptual architecture with practical application considerations.
System DesignOnsite - MetaMediumMachine Learning EngineerDesign weapon-selling ad detection from posts
This question tests the ability to design an end-to-end multimodal classification system for detecting policy-violating content (weapon sales) while balancing latency, precision, and robustness against adversarial evasion.
ML System DesignOnsiteComputer VisionNLPMultimodal Learning - MetaHardMachine Learning EngineerDiscuss Projects, Failures, and Growth
This question evaluates interpersonal and leadership competencies—communication, teamwork, conflict resolution, accountability, resilience, and growth mindset—relevant to a Machine Learning Engineer and is categorized under Behavioral & Leadership.
Behavioral & LeadershipOnsite - MetaHardMachine Learning EngineerFind A Low-Quality Annotator From Label Data
This problem tests your ability to clean noisy labeling datasets and design metrics to detect annotator bias or performance degradation.
Data Manipulation (SQL/Python)OnsitePandasData CleaningMetrics - MetaHardMachine Learning EngineerFind Maximum Unique-Character Subset
This problem tests your ability to use bitmasking and backtracking with pruning to solve a variation of the maximum set packing problem under character constraints.
Coding & AlgorithmsOnsiteBitmaskingBacktrackingRecursion - MetaHardMachine Learning EngineerFind Words Containing Other Words
This problem tests your ability to efficiently identify all words in a list that contain at least one other word from the same list as a substring, utilizing optimized string processing or data structures like Tries.
Coding & AlgorithmsOnsiteStringsTrieOptimization - MetaMediumMachine Learning EngineerFind kth smallest in sorted matrix
This question evaluates algorithmic problem-solving and data-structure knowledge, focusing on selection in a row-and-column-sorted matrix and the comparative use of min-heaps versus value-space binary search along with time/space complexity and edge-case analysis.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerFind longest palindromic substring
This problem tests your ability to optimize string processing algorithms, specifically moving from a brute-force approach to an efficient expansion-based technique for identifying palindromic patterns.
Coding & AlgorithmsOnsiteStringsDynamic ProgrammingTwo Pointers - MetaMediumMachine Learning EngineerFind target using robot movement API
This question evaluates a candidate's ability to design exploration and state-space search strategies for a robot operating in an unknown, partially blocked 2D grid using a constrained movement and sensing API, testing competency in traversal algorithms, state management, and handling limited observability.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerGenerate unique permutations with duplicates
This question evaluates a candidate's understanding of permutation generation and duplicate handling within algorithm design, testing combinatorial reasoning, correctness proofs, and time/space complexity analysis.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerImplement BST Iterator and Ticket Queue
This problem tests your ability to implement an iterator for a Binary Search Tree (BST) using controlled traversal, and your skill in designing a priority-based queue that handles multi-criteria ordering.
Coding & AlgorithmsOnsiteTreesHeapsIterator - MetaMediumMachine Learning EngineerImplement LRU cache and copy random list
This problem tests your ability to handle complex pointer manipulation through linked-list deep copying and your proficiency in designing a thread-safe-capable cache using hash maps and doubly-linked lists.
Coding & AlgorithmsOnsiteLinked ListsHash MapsLRU Cache - MetaMediumMachine Learning EngineerImplement weighted random sampling with preprocessing
This problem tests your ability to optimize weighted random selection using binary search on prefix sums, balancing preprocessing time against query latency.
Coding & AlgorithmsOnsiteProbabilityBinary SearchPrefix Sum - 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 EngineerMaximize one stock trade profit
This question evaluates algorithm design skills for single-pass array processing, streaming data handling, and time/space complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsOnsite - MetaMediumMachine Learning EngineerMaximize unique characters from a word list
This problem tests your ability to use recursion with backtracking or bitmasking to find the optimal subset of strings that maximize the total count of unique characters.
Coding & AlgorithmsOnsiteBacktrackingBitmaskingRecursion - MetaHardMachine Learning EngineerOptimize a Fifteen-Sum Card Strategy
This problem tests your ability to optimize a combinatorial search space through memoization and dynamic programming to determine the maximum win rate of a card-based strategy.
Coding & AlgorithmsOnsiteCombinatoricsProbabilityRecursion - MetaMediumMachine Learning EngineerOptimize ribbon piece length by binary search
This question evaluates understanding of binary-search-on-answer techniques and monotonic predicates, complexity analysis, and implementation-level handling of integer overflow and extreme edge cases in numeric partitioning, situated in the coding and algorithms domain and emphasizing practical implementation considerations rather than purely theoretical proofs. It is commonly asked because it reveals whether an interviewee can reason about monotonicity and design an O(n log max(lengths)) solution that remains robust under very large k, large element values, and potential integer overflow.
Coding & AlgorithmsOnsite - 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 - MetaEasyMachine Learning EngineerSolve Tree Views, Columns, and Calculator
This problem tests your ability to perform recursive binary tree traversals to extract horizontal and vertical views, followed by implementing a stack-based parser to handle mathematical expressions with standard order of operations.
Coding & AlgorithmsOnsiteBinary TreesBFSStack - MetaMediumMachine Learning EngineerSolve linked list, tree, and grid problems
This set evaluates mastery of core data structures and algorithmic reasoning across singly linked lists (cycle entry detection), binary search trees with parent pointers (in-order successor), constant-time state tracking for n×n game boards, and grid connectivity for maximizing component size, emphasizing pointer manipulation, traversal logic, state-design, and component analysis. Commonly asked to gauge proficiency in Coding & Algorithms and complexity analysis, these problems test both conceptual understanding of traversal and connectivity principles and practical application of space- and time-efficient implementations under typical interview constraints.
Coding & AlgorithmsOnsite
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