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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 331–360 of 782 results
- 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-ad harmful content detection system
This question assesses the ability to architect a scalable, multi-modal machine learning pipeline for detecting policy-violating content (weapon advertisements) within a high-throughput ad-delivery system.
System DesignTechnical ScreenMulti-modal MLComputer VisionNLP - 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 image and multimodal generation systems
This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.
ML System DesignTechnical Screen - 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 - MetaMediumMachine Learning EngineerDiscuss Research Experience and Challenges
This question evaluates research design, experimental methodology, problem formulation, ownership, collaboration, prioritization, and the ability to learn from failed or ambiguous outcomes within the Machine Learning Engineer role, encompassing communication and leadership competencies.
Behavioral & LeadershipTechnical Screen - 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 a String Containing Another
This question evaluates proficiency in string algorithms, substring search techniques, and algorithmic complexity analysis within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - 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 - 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 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 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 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 Sparse Matrix Operations
This question tests the ability to design an efficient data structure for sparse matrix storage and implement optimized multiplication between a sparse matrix and a dense vector.
Coding & AlgorithmsTechnical ScreenData StructuresLinear AlgebraOptimization - MetaMediumMachine Learning EngineerImplement exponentiation and fill grid distances
This problem tests proficiency in efficient modular exponentiation (binary exponentiation) and multi-source Breadth-First Search (BFS) for computing shortest distances in a grid.
Coding & AlgorithmsOnline AssessmentBFSModular ArithmeticDivide and Conquer - MetaMediumMachine Learning EngineerImplement exponentiation and link tree neighbors
This question evaluates numerical algorithm implementation and in-place tree traversal skills, specifically fast exponentiation with careful edge-case and precision handling and iterative linking of next pointers in a perfect binary tree.
Coding & AlgorithmsTechnical 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 - 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
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