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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
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- 8
- Languages
Showing 361–390 of 782 results
- 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 - 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 repeated-value vectors and compute exclusive times
This question evaluates proficiency in data-structure and algorithmic concepts such as run-length encoding for compressed vectors and interval/nesting reasoning for computing exclusive execution times.
Coding & AlgorithmsTechnical Screen - 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 - MetaMediumMachine Learning EngineerPrevent Private Code Leakage in Coding Agents
This problem tests the ability to design an ML-driven safeguard system that prevents private codebase exposure during the training, fine-tuning, and inference phases of a coding agent.
ML System DesignTechnical ScreenMLOpsSecurityPrivacy - MetaMediumMachine Learning EngineerRead Current and Historical Field Values with Expiration
Design an in-memory key-field-value store that supports historical queries, time-to-live (TTL) expiration, and field deletions without corrupting past states.
Coding & AlgorithmsOnline AssessmentBinary SearchDesignHash Table - 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 - MetaHardMachine Learning EngineerSimulate Monster Team Battles
This question tests the ability to implement a stateful, turn-based simulation engine using object-oriented principles, efficient queue management, and rule-based damage calculations.
Coding & AlgorithmsTechnical ScreenSimulationObject-Oriented DesignSorting - 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 - MetaEasyMachine Learning EngineerSolve four OA string/array/matrix/graph tasks
This problem tests foundational proficiency in four distinct domains: string processing, array simulation, matrix transformation, and graph traversal.
Coding & AlgorithmsOnline AssessmentStringsSimulationBFS - 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 - MetaMediumMachine Learning EngineerSolve matrix components, median, and traversals
This question evaluates algorithmic problem-solving across grid and graph algorithms, selection algorithms, and traversal techniques—specifically connected-component detection in binary matrices, median-finding across two sorted arrays, and BFS/DFS traversal variants—measuring competence in data structures, complexity analysis, and handling edge cases. Commonly asked in Coding & Algorithms interviews for Machine Learning Engineer roles, it assesses efficiency and correctness concerns such as time and space complexity and traversal orders, testing both conceptual understanding of algorithmic principles and practical implementation distinctions like iterative versus recursive variants.
Coding & AlgorithmsTechnical Screen - MetaMediumMachine Learning EngineerSolve matrix diagonal and sliding-window statistics
Solve matrix diagonal and sliding-window statistics evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen - MetaEasyMachine Learning EngineerSolve two linked list/array tasks
This problem tests your ability to manipulate linked list pointers to detect cycles and solve a classic array-based problem using the two-pointer technique to find specific target sums.
Coding & AlgorithmsTechnical ScreenLinked ListTwo PointersArrays - MicrosoftMediumMachine Learning EngineerCalibrate LLM output to match Word formatting
This question tests the ability to design an end-to-end system for enforcing strict structured formatting (e.g., Docx/Markdown) on LLM-generated text, focusing on constraint enforcement and reliability.
ML System DesignOnsiteLLMSystem DesignConstrained Decoding - MicrosoftMediumMachine Learning EngineerClean OCR data and build an LLM dataset
This problem tests the ability to design an automated pipeline for cleaning noisy OCR text output and preparing it for supervised fine-tuning of an LLM.
Machine LearningOnsiteData EngineeringOCRLLM Fine-tuning - MicrosoftMediumMachine Learning EngineerCompare preference alignment methods for LLMs
This question tests your conceptual understanding of alignment techniques, your ability to evaluate trade-offs between stability and performance, and your approach to measuring model safety and utility.
Machine LearningOnsiteRLHFDPOAlignment - MicrosoftMediumMachine Learning EngineerCompute precision/recall from a flaky top-k API
This question evaluates understanding of binary classification evaluation metrics (precision and recall) and competency in handling unreliable API behavior for robust metric computation, testing both conceptual understanding and practical application.
Software Engineering FundamentalsTechnical Screen - MicrosoftMediumMachine Learning EngineerDescribe motivation, ownership, and conflict
This question evaluates motivation, ownership, conflict-resolution, and leadership communication competencies, focusing on interpersonal dynamics and responsibility relevant to a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - MicrosoftMediumMachine Learning EngineerDesign a Product Search System
This question evaluates skills in designing scalable, low-latency product search systems, covering information retrieval, ranking model design, feature generation, data ingestion and indexing pipelines, and serving infrastructure.
ML System DesignOnsite - MicrosoftMediumMachine Learning EngineerDesign a RAG-based assistant service
This question assesses the ability to architect a scalable Retrieval-Augmented Generation (RAG) system, focusing on data indexing, retrieval strategy, and mitigating common LLM issues like hallucinations and safety.
ML System DesignOnsiteRAGLLMVector Databases - MicrosoftMediumMachine Learning EngineerDesign a Retrieval-Grounded Local Sports News Recommender
This question tests the ability to design an end-to-end RAG system for news, focusing on temporal data relevance, hybrid retrieval strategies, and mitigation of LLM hallucinations.
ML System DesignHR ScreenRAGRecommendationSystem Design - MicrosoftMediumMachine Learning EngineerDesign chat and online chess
This problem tests your ability to design a distributed system that supports low-latency, real-time interactions for both chat messaging and synchronized game state for a chess platform.
System DesignOnsiteWebSocketsScalabilityConcurrency - MicrosoftMediumMachine Learning EngineerDesign email ranking and summarization in Outlook
This problem tests the ability to architect a scalable ML pipeline that balances personalized email prioritization with LLM-based summarization while addressing low-latency requirements and data safety.
ML System DesignOnsiteRankingGenerative AISystem Design - MicrosoftMediumMachine Learning EngineerDesign quality checks for spreadsheet LLM data
This problem tests the ability to design an end-to-end data validation pipeline for spreadsheet data and determine the necessity of fine-tuning a Large Language Model (LLM) for specific structured data tasks.
ML System DesignTechnical ScreenLLMData ValidationFine-tuning - MicrosoftEasyMachine Learning EngineerExplain Transformers and deploy an LLM safely
This question evaluates understanding of Transformer architectures and practical LLM deployment competencies, covering attention mechanisms, token and positional representations, computational complexity, and production concerns like latency, cost, quality, safety, and privacy.
ML System DesignTechnical Screen - MicrosoftMediumMachine Learning EngineerExplain a project deeply
This question assesses the candidate's depth of ownership over a previous machine learning project, specifically testing their ability to articulate system design choices, technical trade-offs, and operational resilience.
Software Engineering FundamentalsOnsiteSystem DesignMLOpsArchitecture
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