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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
- Questions
- 546
- Companies
- 11
- Topics
- 8
- Languages
Showing 61–90 of 782 results
- AppleMediumMachine Learning EngineerDesign a grounded voice assistant
This problem evaluates your ability to design a retrieval-augmented generation (RAG) system for a voice assistant, focusing on grounding model outputs in trusted sources to prevent hallucinations.
ML System DesignOnsiteRAGLLMSystem Design - AppleMediumMachine Learning EngineerDesign a multimodal RAG assistant
This question evaluates system design and machine learning engineering skills for building a multimodal Retrieval-Augmented Generation (RAG) assistant, covering competencies in data ingestion and preprocessing across modalities, indexing and retrieval strategies, embeddings and re-ranking, grounding/prompting with citations, and evaluation and failure-mode mitigation. It is commonly asked to gauge an engineer's ability to architect scalable, grounded QA pipelines that balance retrieval quality, latency, and cost; it sits in the System Design domain and tests both high-level architectural reasoning and practical implementation considerations.
System DesignTechnical Screen - AppleHardMachine Learning EngineerDesign a streaming embedding-based classifier
This question evaluates a candidate's ability to design end-to-end streaming machine learning systems, including online text preprocessing, tokenization, embedding generation, continuous model training, and low-latency classification serving.
ML System DesignTechnical Screen - AppleMediumMachine Learning EngineerExplain Vision Encoders and LLM Bottlenecks
This question evaluates understanding of vision encoders and their role in computer vision and multimodal models, familiarity with typical training approaches for encoders, and the ability to identify inference bottlenecks in large language models as well as knowledge of memory and latency optimization considerations.
Machine LearningTechnical Screen - AppleHardMachine Learning EngineerExplain annotation agreement and LLM vs human judges
This question tests a machine learning engineer's understanding of annotation reliability metrics and the trade-offs between human and LLM-based evaluation. It assesses conceptual and applied knowledge of inter-annotator agreement, metric selection across label types, and the validity of automated judges in model evaluation workflows.
Machine LearningTechnical Screen - AppleMediumMachine Learning EngineerExplain classification lifecycle and CTR modeling
This question evaluates competency in end-to-end supervised classification and production machine learning systems, covering text feature engineering (bag-of-words), ensemble evaluation via out-of-bag methods, CTR prediction workflows including calibration and evaluation, and operational debugging of deployed models.
Machine LearningOnsite - AppleMediumMachine Learning EngineerFind Minimum Processing Rate
This problem tests your ability to use binary search on a search space to find an optimal minimum processing rate that satisfies a global deadline constraint.
Coding & AlgorithmsTechnical ScreenBinary SearchGreedyOptimization - AppleMediumMachine Learning EngineerHow do you align ambiguous cross-functional projects?
This question evaluates communication, cross-functional collaboration, stakeholder management, project scoping under ambiguity, product-technical integration, and domain-specific experience with AI-agent-based systems and designer collaboration.
Behavioral & LeadershipTechnical Screen - AppleMediumMachine Learning EngineerHow do you prioritize and influence?
This question evaluates prioritization, stakeholder influence, resource negotiation, cross-functional collaboration, product judgment, and execution under constraints for an AI/ML engineering role.
Behavioral & LeadershipOnsite - AppleMediumMachine Learning EngineerImplement Kubernetes Service Dependency Queries
This question evaluates proficiency with data structures and algorithms for service inventory operations, including deterministic filtering, attribute matching, graph traversal for transitive dependency resolution, and status propagation across service graphs.
Coding & AlgorithmsOnsite - AppleEasyMachine Learning EngineerImplement Masked Multi-Head Self-Attention
This question tests the ability to implement the core mechanics of a Transformer's multi-head attention mechanism, specifically focusing on linear projections, attention weight scaling, and causal masking.
Machine LearningTechnical ScreenTransformersDeep LearningAttention - AppleMediumMachine Learning EngineerImplement TF-IDF scoring for documents
This question evaluates understanding of information retrieval and text representation concepts—specifically TF–IDF weighting, tokenization, term frequency and document frequency—and measures competency in implementing an efficient scoring and ranking algorithm.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerImplement bag-of-words similarity search from scratch
This question evaluates knowledge and implementation skills in text processing, vector-space models, and information retrieval, covering tokenization, term-frequency and TF–IDF weighting, cosine similarity, and ranking for similarity search within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerImplement permutations and image retrieval
This question evaluates algorithmic implementation and computational thinking across combinatorics, image processing, and vector similarity—specifically testing permutation generation, 2D convolution with explicit boundary handling, and nearest-neighbor retrieval for image embeddings.
Coding & AlgorithmsOnsite - AppleMediumMachine Learning EngineerOptimize image filters on device
This question evaluates competency in on-device machine learning and performance engineering, covering algorithm choice, numerical precision trade-offs, hardware utilization (CPU/GPU/accelerator), memory layout, tiling/streaming, and trade-offs among latency, memory overhead, and battery impact.
ML System DesignOnsite - AppleMediumMachine Learning EngineerRotate a Matrix In Place
This question evaluates proficiency in in-place matrix and array manipulation, spatial data transformation, and reasoning about time and space complexity.
Coding & AlgorithmsTechnical Screen - AppleMediumMachine Learning EngineerSolve stock and banana problems
This question evaluates algorithmic problem-solving skills related to array processing, optimization under constraints, and reasoning about performance trade-offs by presenting a maximum single-transaction stock profit problem and a minimum banana-eating speed problem.
Coding & AlgorithmsOnsite - AppleHardMachine Learning EngineerWalk Through an Agentic PDF Evaluation Codebase
This question tests your ability to navigate, critique, and extend a modular codebase designed for evaluating agentic PDF processing pipelines, focusing on evaluation metrics and robust system architecture.
Software Engineering FundamentalsTechnical ScreenAgentic-WorkflowsEvaluationLLM-Systems - AppleMediumMachine Learning EngineerWrite a generator for substring pattern matches
This question evaluates understanding of string pattern matching and generator/iterator design, testing skills in handling overlapping matches, streaming output, and worst-case time complexity considerations.
Coding & AlgorithmsTechnical Screen - Applied IntuitionMediumMachine Learning EngineerDebug a GPT training pipeline
This question evaluates debugging and implementation skills for transformer-based training pipelines, specifically attention masking, training-loop correctness, positional encoding integration, and the ability to design unit tests that catch such bugs.
ML System DesignTechnical Screen - Applied IntuitionMediumMachine Learning EngineerDesign Ordered CUDA Reduction
This question evaluates understanding of parallel GPU programming, deterministic reduction ordering, and numerical stability in floating-point arithmetic, within the domain of parallel algorithms, GPU/CUDA programming, and numerical computing for a Software Engineering Fundamentals / Machine Learning Engineer role.
Software Engineering FundamentalsTechnical Screen - Applied IntuitionMediumMachine Learning EngineerImplement a Fixed-Capacity Deque
This question evaluates proficiency with double-ended queue semantics, fixed-capacity data structure design, constant-time (O(1)) operations, and correct handling of boundary conditions and wraparound.
Coding & AlgorithmsTechnical Screen - Applied IntuitionMediumMachine Learning EngineerImplement and explain positional encoding
This question evaluates knowledge of positional encoding mechanisms for Transformer language models, covering embedding mathematics, tensor shapes and broadcasting, PyTorch implementation details, expected training and inference symptoms when positional information is omitted, and methods for empirical verification and ablation.
Machine LearningTechnical Screen - Applied IntuitionHardMachine Learning EngineerValidate Nested Configuration Objects
This question evaluates schema validation skills including recursive data structures, primitive and composite type checking, and structured error reporting for configuration objects, categorized under Coding & Algorithms for a Machine Learning Engineer role and focused on practical implementation.
Coding & AlgorithmsTechnical Screen - AtlassianMediumMachine Learning EngineerAssign bookings to minimum tennis courts
This problem tests your ability to manage overlapping time intervals using a greedy approach, specifically requiring the calculation of the maximum number of concurrent bookings to determine the minimum resource count.
Coding & AlgorithmsOnsiteGreedySortingIntervals - AtlassianHardMachine Learning EngineerCompute a moving average on a stream
This problem tests your ability to maintain a running average over a sliding window of size K in a high-throughput streaming environment while managing numerical precision and memory efficiency.
Coding & AlgorithmsOnsiteAlgorithmsData StructuresStreaming - AtlassianMediumMachine Learning EngineerCompute inverse-depth weighted sum of a nested list
This problem tests your ability to traverse nested list structures using recursion or iterative stacks and compute a value based on the nesting depth of individual elements.
Coding & AlgorithmsOnsiteRecursionTreesData Structures - AtlassianMediumMachine Learning EngineerCompute moving average over last N stream
This question evaluates understanding of streaming algorithms and sliding-window aggregation within the Coding & Algorithms domain, focusing on incremental computation, state management, and numerical stability when computing moving averages.
Coding & AlgorithmsOnsite - AtlassianMediumMachine Learning EngineerDesign Jira bug-to-team classification system
This question tests the ability to design an end-to-end ML pipeline for classifying incoming Jira tickets to appropriate engineering teams based on textual and historical metadata.
ML System DesignOnsiteNLPClassificationScalability - AtlassianMediumMachine Learning EngineerDesign O(1) cache and moving average
This problem tests your ability to maintain constant-time complexity for both cache management and sliding-window statistics by using a combination of a doubly-linked list and hash maps.
Coding & AlgorithmsOnsiteLRU CacheData StructuresSliding Window
Apple
Applied Intuition
Atlassian