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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 91–120 of 782 results
- AtlassianMediumMachine Learning EngineerDesign a Jira+Confluence RAG assistant
This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.
ML System DesignOnsite - AtlassianMediumMachine Learning EngineerDesign a scalable chatbot platform
This problem tests your ability to architect a scalable, stateful chatbot system that integrates RAG (Retrieval-Augmented Generation) with low-latency conversational requirements.
System DesignOnsiteLLMRAGScalability - AtlassianHardMachine Learning EngineerDesign an ML-powered search system
This question tests the ability to design a scalable, low-latency, and relevant search system for internal enterprise documents, focusing on the integration of hybrid search (keyword + semantic) and ranking strategies.
ML System DesignOnsiteInformation RetrievalRankingEmbeddings - AtlassianMediumMachine Learning EngineerDesign an internal data-access chatbot
This question evaluates a candidate's ability to design production-ready ML systems that integrate retrieval-augmented generation, secure access control, tool-based workflows, data privacy, auditability, and low-latency interaction.
ML System DesignOnsite - AtlassianMediumMachine Learning EngineerFind LCA in a tree and extend to a DAG
This problem tests your understanding of tree traversal, graph theory, and how to generalize path-finding algorithms from trees to directed acyclic graphs (DAGs).
Coding & AlgorithmsOnsiteGraphsLCATrees - AtlassianHardMachine Learning EngineerFind a secret word using match feedback
Design an optimal querying strategy to identify a secret word from a candidate vocabulary using exact-match character feedback under a strict query budget.
Coding & AlgorithmsOnsiteAlgorithmsMinimaxInformation Theory - AuroraHardMachine Learning EngineerApplying LLMs to Autonomous Driving Despite Hallucination and Real-Time Limits
An open-ended ML system design discussion on how large language models and vision-language models could be used in autonomous driving, including whether one could actually drive the vehicle. It tests mapping off-board and on-vehicle uses, containing hallucination and latency in a safety-critical stack, and planning evaluation.
ML System DesignTechnical Screen - AuroraHardMachine Learning EngineerPitching an LLM-for-Autonomy Initiative to Leadership to Win Scope
A leadership question asking how you would present an initiative to bring language models into a self-driving program to leadership and win scope for it. It tests problem framing, gathering evidence, handling risk in a safety-critical organization, aligning stakeholders, and making a concrete, phased ask.
Behavioral & LeadershipTechnical Screen - AutodeskMediumMachine Learning EngineerExplain 3D geometry data
Explain 3D geometry data evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - BeaconfireEasyMachine Learning EngineerCheck Whether a String Is Built by Repetition
This problem tests your ability to detect periodic string patterns using string manipulation or mathematical properties, specifically focusing on efficient substring identification.
Coding & AlgorithmsTechnical ScreenStringsPattern MatchingAlgorithms - ByteDanceHardMachine Learning EngineerCombined Median of Two Sorted Integer Lists in Logarithmic Time
From a ByteDance technical screen for a machine learning engineer role: given two sorted integer arrays, return the median of all their elements combined, averaging the two middle values when the total count is even. It tests reasoning about sorted order under a logarithmic time target and edge cases such as an empty array.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumMachine Learning EngineerCompute Sentence Similarity
This question evaluates understanding of sentence-level and token-level embedding techniques, text preprocessing, similarity metrics, edge-case handling for empty or unknown tokens, and trade-offs between pretrained sentence encoders and averaged word embeddings.
Machine LearningTechnical Screen - ByteDanceHardMachine Learning EngineerCount Special Subarrays by Modular Frequency
This question evaluates the ability to apply modular arithmetic, frequency tracking, and prefix-count reasoning to efficiently count subarrays that satisfy a congruence condition, testing algorithmic problem-solving and scalable complexity management.
Coding & AlgorithmsOnsite - ByteDanceMediumMachine Learning EngineerDesign Self-Dealing Detection for Marketplaces
This question evaluates competency in machine learning system design for fraud detection, focusing on modeling buyer–seller–item–transaction relationships, feature engineering across graph, temporal, behavioral and account-linking signals, and production concerns; it is in the ML System Design domain and emphasizes practical application and system-level architecture over purely theoretical concepts. It is commonly asked to assess a candidate’s ability to reason about data collection and labeling, model and rule trade-offs, evaluation metrics, scalability and deployment trade-offs when detecting coordinated or self-dealing activity in marketplace environments.
ML System DesignTechnical Screen - ByteDanceMediumMachine Learning EngineerDesign an Enterprise Tool-Using Agent
This question evaluates a candidate's ability to design production-grade LLM agents that integrate external tools, manage long-running and branching workflows, persist complex state, and ensure safety, observability, and reliable evaluation in enterprise settings.
ML System DesignTechnical Screen - ByteDanceMediumMachine Learning EngineerExplain LLM generational improvements, multimodal LLMs, and normalization in LLMs
Three conceptual LLM questions: the main improvements across generations of a model family as described in technical reports, how multimodal image-and-text models are built and trained, and why modern LLMs use layer normalization or RMSNorm with pre-norm placement instead of batch normalization.
Machine LearningTechnical Screen - ByteDanceMediumMachine Learning EngineerExplain XGBoost's Overfitting Resistance
This question evaluates understanding of ensemble learning and model generalization, focusing on why gradient-boosted tree ensembles are often less prone to overfitting than single decision trees.
Machine LearningTechnical Screen - ByteDanceHardMachine Learning EngineerImplement a Transformer Decoder from Scratch Without Starter Code
Implement a transformer decoder from scratch with no starter code in about 30 minutes, including embeddings, masked multi-head self-attention, feed-forward layers, residual connections and layer normalization. Tests tensor-shape fluency, causal masking and hands-on deep learning coding for a machine learning engineer interview.
Machine LearningTechnical Screen - ByteDanceMediumMachine Learning EngineerMaximize total catch over K hours from ponds whose yield drops after each hour
Given the current hourly catch of each fishing pond and a budget of K hours, where every hour fished at a pond lowers its catch by one, compute the maximum total number of fish. It tests modeling the declining yields, choosing an optimal schedule, and meeting a time bound better than O(K log N) when K is very large.
Coding & AlgorithmsTechnical Screen - ByteDanceHardMachine Learning EngineerMinimum Number of Rooms to Host a Set of Possibly Overlapping Meetings
Given meetings as half-open start and end time intervals, compute the minimum number of rooms needed so that no room ever hosts two overlapping meetings. Tests interval reasoning, correct handling of back-to-back meetings that share an endpoint, and an efficient approach for up to 100,000 meetings.
Coding & AlgorithmsTechnical Screen - ByteDanceMediumMachine Learning EngineerReverse a Linked List in Groups of K
This problem tests your ability to manipulate pointers in a linked list structure and manage iterative or recursive state transitions for fixed-size segments.
Coding & AlgorithmsTechnical ScreenLinked ListPointersAlgorithms - CadenceMediumMachine Learning EngineerExplain Transformer Positional Encoding
This question tests your conceptual and technical understanding of why Transformers require positional encodings and the mathematical mechanics of different encoding strategies.
Machine LearningTechnical ScreenTransformerAttentionNLP - CadenceMediumMachine Learning EngineerImplement Depth-First Search Traversal
This problem tests your ability to implement a recursive or iterative depth-first search (DFS) while managing state to handle cycles in a graph represented as an adjacency list.
Coding & AlgorithmsTechnical ScreenGraphsDFSRecursion - Capital OneMediumMachine Learning EngineerAnswer leadership scenarios with STAR
This question evaluates leadership, influence, problem-solving, prioritization, resilience, and the ability to deliver measurable results under constraints by eliciting STAR-format behavioral examples relevant to a Machine Learning Engineer role.
Behavioral & LeadershipOnsite - Capital OneHardMachine Learning EngineerCompute minutes since last train departure
This problem tests your ability to manipulate time objects, handle cyclic day-wrap scenarios, and perform efficient searching on sorted lists.
Coding & AlgorithmsOnline AssessmentArraysSortingTime Complexity - Capital OneMediumMachine Learning EngineerDescribe projects, conflicts, and tough stakeholders
This question evaluates project ownership, technical troubleshooting, cross-team communication, conflict resolution, and stakeholder management competencies for a Machine Learning Engineer within the Behavioral & Leadership domain.
Behavioral & LeadershipOnline Assessment - Capital OneMediumMachine Learning EngineerDesign model deployment, monitoring, and low-latency inference
This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.
ML System DesignOnsite - Capital OneMediumMachine Learning EngineerExplain core ML concepts and lifecycle
This question evaluates understanding of core machine learning concepts and lifecycle competencies, including bias–variance tradeoff, parameters versus hyperparameters, batch versus real-time inference, strategies for training at scale, feature engineering, and end-to-end model development.
Machine LearningOnsite - Capital OneEasyMachine Learning EngineerHow would you improve card-type detection?
This question tests your ability to design a modular and maintainable classification system that identifies credit card networks (e.g., Visa, Mastercard) based on raw input data while considering validation and error handling.
Software Engineering FundamentalsOnsiteData ValidationString ManipulationSystem Design - Capital OneMediumMachine Learning EngineerImplement string/matrix simulations and counting pairs
This problem tests string manipulation, simulation of grid-based gravitational collapse, and efficient combinatorial counting of string combinations.
Coding & AlgorithmsOnline AssessmentStringsSimulationHashing
Atlassian
Aurora
Capital One