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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
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- 11
- Topics
- 8
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Showing 331–351 of 351 results
- UberMediumMachine Learning EngineerAnswer leadership questions on tradeoffs and collaboration
This set of behavioral leadership questions evaluates leadership competencies including trade-off decision-making, mentorship, cross-functional collaboration, roadmap prioritization, and conflict resolution for a Machine Learning Engineer within the Behavioral & Leadership domain, focusing on practical application through recounting real project experiences. These prompts are commonly asked to probe both conceptual understanding of trade-offs and organizational dynamics and the practical application of influence, communication, and judgment when aligning stakeholders and prioritizing work.
Behavioral & LeadershipOnsite - UberMediumMachine Learning EngineerChoose K pickup locations minimizing L1 distance
This question evaluates facility-location and spatial optimization skills under the Manhattan (L1) metric, testing algorithm design, combinatorial optimization, and handling geometric data.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerCompute minimal time to finish dependent tasks
This question evaluates understanding of graph algorithms and scheduling, specifically reasoning about task dependencies, parallel execution, and cycle detection in directed graphs.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerCount connected delivery zones
This question evaluates graph traversal and connected-components detection on grids, measuring competence in grid-based algorithms, neighbor connectivity, and algorithmic analysis of time and space complexity.
Coding & AlgorithmsOnsite - UberMediumMachine Learning EngineerDesign a restaurant recommendation system
An Uber MLE onsite ML system design question: design a personalized restaurant recommendation system for a food delivery marketplace's home feed. It tests candidate generation and ranking, feature engineering and training data, cold-start and exploration, low-latency serving, and offline/online evaluation, plus marketplace concerns like availability, ETA, diversity, fairness, and business rules.
ML System DesignOnsite - UberHardMachine Learning EngineerDesign real-time grid ETA for drivers
This question evaluates a candidate's competency in real-time ML system design, covering streaming data ingestion, GPS noise handling and denoising, online feature computation, low-latency model serving, and operational monitoring.
ML System DesignOnsite - UberMediumMachine Learning EngineerImplement a Referral Revenue Tracker
This question evaluates proficiency with data structures and algorithms for hierarchical aggregation and ranking, assessing skills such as maintaining recursive subtree aggregates, handling dynamic inserts, and executing efficient top-k queries; it is in the Coding & Algorithms domain and tests practical application of in-memory data structure design. It is commonly asked to evaluate the ability to design scalable, low-latency in-memory systems that preserve aggregate values across referral trees and support fast query operations while reasoning about time and space complexity trade-offs.
Coding & AlgorithmsOnsite - Walmart LabsEasyMachine Learning EngineerDesign a Conversational Agent for Finding Vacation Rentals
Design a multi-turn assistant that turns a traveler's free-form trip description into structured, grounded rental search results. Tests where to use an LLM versus deterministic search, clarification strategy, safety against hallucination and injection, and evaluation against classic search.
ML System DesignOnsitellm-agentssearchretrieval - Walmart LabsEasyMachine Learning EngineerDesign an Agentic Search Experience
Design an agentic search experience with clarifying dialogue, structured constraints, grounded retrieval, bounded tool use, and evaluation against conventional search.
ML System DesignOnsite - Walmart LabsEasyMachine Learning EngineerDesign an LLM agent with RAG and tools
This problem tests the ability to architect an LLM-based agent capable of retrieving information and performing actions, focusing on orchestration, tool execution, and system reliability.
Machine LearningOnsiteLLMRAGAgentic-Workflow - Walmart LabsEasyMachine Learning EngineerDesign an item category prediction system
This question tests the ability to design an end-to-end classification system for assigning retail items into a hierarchical category structure, focusing on feature selection, model architecture, and production deployment.
ML System DesignOnsiteClassificationNLPMulti-modal - Walmart LabsEasyMachine Learning EngineerFind first non-repeating character
This problem tests your ability to use hash maps or frequency arrays to achieve optimal time complexity when processing character streams or strings.
Coding & AlgorithmsOnsiteStringsHash MapFrequency Array - Walmart LabsEasyMachine Learning EngineerResolve Technical Conflict and Align Across Teams
Practice explaining technical conflict, cross-team priorities, decision ownership, dissent, and the follow-through that turns agreement into working alignment.
Behavioral & LeadershipOnsite - WaymoHardMachine Learning EngineerGenerate Instructions for a Blind Maze Robot
This question evaluates a candidate's ability to design algorithmic strategies for state-space synchronization, graph traversal, and reasoning under partial observability in grid environments.
Coding & AlgorithmsOnsite - WaymoMediumMachine Learning EngineerReturn All Words Matching Each Prefix From a Word List
Build a structure over a word list that answers prefix queries, returning every distinct word that starts with each prefix in lexicographic order. Tests prefix-tree design, deduplication, and answering many queries without rescanning the whole list.
Coding & AlgorithmsOnsite - xAIHardMachine Learning EngineerConstant-Time Insert, Remove, and Uniform Random Sampling
This question evaluates the ability to design a data structure that supports constant-time insertions, removals, and uniform random sampling, testing concepts such as hashing, indexed storage, and randomized selection within the coding and algorithms domain.
Coding & AlgorithmsOnsite - xAIMediumMachine Learning EngineerDynamic Batching Decode Loop for a Mock Language Model with Slot Refilling
An LLM inference coding question: write the generation loop for a mock language model that returns one next token per sequence in a batch. Requests finish at different steps on a stop token or a max-token limit, and freed batch slots must be refilled from a waiting queue, testing state tracking and batching logic.
Software Engineering FundamentalsOnsite - xAIMediumMachine Learning EngineerExplain Data Collection Choices and Their Impact on Downstream Model Performance
A machine learning screening question: explain how the data for a project on your resume was collected and processed, then discuss how data choices affect performance on downstream tasks. It tests first-hand knowledge of data pipelines, causal reasoning about data quality, and how to measure data effects.
Machine LearningOnsite - xAIMediumMachine Learning EngineerFind All Faulty GPU Nodes with Parallel Group Tests That Cannot Share Nodes
An algorithm design question about finding every faulty GPU node when the only tool is a group test that fails if any node in the set is bad. Tests need at least two nodes and parallel tests cannot share nodes, so it tests adaptive testing strategies, identifiability, and cost in rounds and tests.
Software Engineering FundamentalsOnsite - xAIHardMachine Learning EngineerGreedy Longest-Match Tokenizer for an LLM Data Pipeline
This question evaluates understanding of string-processing and greedy maximal-munch tokenization, as well as competency in designing efficient prefix-matching data structures and handling large-scale text preprocessing for LLM pipelines.
Coding & AlgorithmsOnsite - xAIMediumMachine Learning EngineerImplement dynamic batching for token decoding
This question evaluates dynamic batching, per-request state management, and sequence-decoding correctness for language-model inference, including handling stop conditions, max-token limits, and maintaining a correct slot-to-request mapping.
Coding & AlgorithmsOnsite
Uber
Walmart Labs
Waymo
xAI