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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 121–150 of 782 results
- Capital OneMediumMachine Learning EngineerMaximize grid-path expression and count sawtooth subarrays
This multi-part question evaluates dynamic programming and state management for constrained grid traversal with left-to-right expression evaluation, alongside parity-based array analysis and combinatorial counting of contiguous subarrays.
Coding & AlgorithmsHR Screen - Capital OneMediumMachine Learning EngineerReturn all root-to-leaf tree paths
This question evaluates competence with binary tree structures, path enumeration and traversal techniques along with the ability to represent traversal results as formatted strings.
Coding & AlgorithmsOnsite - Capital OneMediumMachine Learning EngineerRotate matrix and find max lamp coverage
This question evaluates array and matrix manipulation, in-place algorithm design, interval processing (sweep-line) and interval merging skills, testing competencies in algorithm design, complexity analysis, and robust edge-case reasoning within the Coding & Algorithms domain.
Coding & AlgorithmsOnline Assessment - Capital OneMediumMachine Learning EngineerSimulate round-robin package assignment to servers
This problem tests array indexing manipulation and logic for stateful resource allocation, specifically handling circular queues with dynamic availability constraints.
Coding & AlgorithmsOnline AssessmentArraysSimulationQueueing - Capital OneMediumMachine Learning EngineerSolve Four Coding Assessment Tasks
This multi-part coding question evaluates array manipulation, algorithm simulation, deterministic spatial placement, and constrained optimization competencies across tasks involving cumulative thresholds, iterative subtraction sequences, fixed-shape grid placement, and minimal-increment adjustments for monotonic patterns.
Coding & AlgorithmsOnline Assessment - Capital OneEasyMachine Learning EngineerSolve OA tasks on string, grid path, subarrays
This problem tests proficiency in string manipulation, grid traversal with condition-based movement, and linear-time subarray analysis.
Coding & AlgorithmsOnline AssessmentArraysDynamic ProgrammingGrid Traversal - Capital OneHardMachine Learning EngineerSupport updates and count target-sum pairs
This problem tests the ability to maintain frequency counts of elements in a dynamic dataset and perform efficient range-sum queries for pairs that satisfy a target condition.
Coding & AlgorithmsOnline AssessmentHash MapData StructuresFrequency Array - Capital OneMediumMachine Learning EngineerValidate virtual credit card transactions from encoded IDs
This question evaluates understanding of virtual credit card product trade-offs and the ability to design data models and deterministic validation logic for encoded transaction attributes, testing competencies in payment systems, data encoding/decoding, and rule-based validation.
System DesignOnsite - CitadelHardMachine Learning EngineerMake a hard MoE router differentiable
Make a hard MoE router differentiable 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 - CognitivMediumMachine Learning EngineerDesign a Real-Time Feature Store
This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.
ML System DesignTechnical Screen - CognitivMediumMachine Learning EngineerMaintain a Streaming Median
This question evaluates understanding of streaming algorithms and dynamic data structures for maintaining order statistics, along with analysis of time complexity and memory trade-offs.
Coding & AlgorithmsTechnical Screen - CohereMediumMachine Learning EngineerBuild an LLM-driven text-game agent
This question tests the ability to design a robust pipeline for an LLM-based agent interacting with a stateful environment, focusing on context management, output validation, and error recovery.
Software Engineering FundamentalsOnsiteLLMPrompt EngineeringSystem Design - CohereMediumMachine Learning EngineerDefend Your Own Research Paper: Experimental Setup, Practicality and Later Work
A research paper round where you present your own published paper to an interviewer who already knows it and questions its weaknesses. Prepare to judge whether the experimental setup was reasonable, whether the results are practical, and what the field has learned since publication, all with specific, honest self-critique.
Machine LearningOnsite - CohereMediumMachine Learning EngineerDesign How an LLM Detects Questions Beyond Its Training Cutoff and Says So
Design how a large language model recognizes questions that depend on information after its training cutoff and tells the user where its knowledge ends. The question tests data construction for calibrated abstention, prompt versus fine-tuning versus retrieval trade-offs, and evaluating both over-refusal and hallucination.
ML System DesignOnsite - CohereMediumMachine Learning EngineerDesign a Post-Training Pipeline to Improve a 7B Model's Coding for an Enterprise
Design an end-to-end post-training pipeline that improves a 7B language model's coding ability for an enterprise customer, covering data collection, SFT, RLHF, evaluation, serving and a production feedback loop. The question probes dataset construction, reward model choice and why RLHF is still needed when SFT can learn from preference data.
ML System DesignOnsite - CohereMediumMachine Learning EngineerExplain LLM training, RL, and evaluation
This question tests your conceptual understanding of the end-to-end LLM lifecycle, specifically the transition from raw text pre-training to alignment and evaluation.
Machine LearningOnsiteLLMRLHFPPO - CohereMediumMachine Learning EngineerExplain Top-k and Top-p Decoding
This question tests your conceptual and mathematical understanding of Top-k and Top-p (nucleus) sampling, focusing on how they modify the output probability distribution of autoregressive language models to balance creativity and coherence.
Machine LearningOnsiteNLPGenerative AISampling - CohereMediumMachine Learning EngineerImplement Autoregressive Decoding Strategies
This problem tests your ability to implement common sequence decoding strategies used in large language models by manipulating token probability distributions.
Coding & AlgorithmsOnsiteLLMSamplingSoftmax - CohereMediumMachine Learning EngineerImprove LLM reasoning for a domain task
This problem tests your ability to design a robust pipeline for an LLM-based system that requires multi-step reasoning in a specialized domain (e.g., technical legal analysis or medical diagnosis).
ML System DesignOnsiteLLMReasoningRAG - CohereMediumMachine Learning EngineerReplace a Heuristic Terminal Text Game's Python Logic With LLM Calls
A live coding round: replace the heuristic Python implementation of a terminal text game with one driven by an LLM while keeping the game playable end to end. AI assistants are allowed, but the round judges your own implementation and debugging, including validating model output, fallbacks, and tests with a fake model.
Software Engineering FundamentalsOnsite - CoinbaseMediumMachine Learning EngineerBuild a baseline classification model from messy data
This question evaluates skills in practical machine learning engineering, including data cleaning, preprocessing, feature selection, handling mixed numeric and categorical features, and baseline model construction for a binary classification task.
Machine LearningOnsite - CoinbaseHardMachine Learning EngineerExplain precision/recall and compute NN output
This question tests the ability to calculate classification performance metrics, explain ensemble model dynamics, and perform manual forward propagation through a small neural network architecture.
Machine LearningOnline AssessmentClassificationNeural NetworksEnsemble Methods - CoinbaseHardMachine Learning EngineerImplement local maxima, bagging, and k-means
This problem tests array traversal for local maxima detection, statistical resampling techniques for bagging, and iterative convergence for k-means clustering.
Coding & AlgorithmsOnline AssessmentAlgorithmsClusteringEnsemble Methods - CoinbaseMediumMachine Learning EngineerWrite SQL to rank top products per category
This question evaluates SQL proficiency in aggregation, time-based filtering, joining transactional and reference tables, and ranking/windowing techniques to compute and deterministically order product revenue with tie-breaking by product_id.
Coding & AlgorithmsOnsite - CreditkarmaMediumMachine Learning EngineerDesign Personalized Promotion Recommendations
This question tests the ability to design an end-to-end recommendation system for personalized financial promotions, focusing on handling sparse conversion data, real-time feature requirements, and feedback loops.
ML System DesignTechnical ScreenRecommendation SystemsRankingFeature Engineering - CreditkarmaMediumMachine Learning EngineerDesign a Revenue Ranking Platform
This problem tests your ability to design a multi-stage ranking system that balances business revenue goals with user relevance, specifically addressing sparse conversion data and long-term user retention.
ML System DesignOnsiteRankingRecommendationMulti-task Learning - DRWMediumMachine Learning EngineerBuild pipeline for imbalanced classification
This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Build pipeline for imbalanced classification states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment - DRWMediumMachine Learning EngineerExplain core ML and DL fundamentals
Explain core ML and DL fundamentals 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 LearningOnline Assessment - DRWMediumMachine Learning EngineerProcess CSV for portfolio returns and metrics
This question evaluates a candidate's competency in time-series data manipulation, financial return computation, portfolio analytics, numerical stability, modular software design (clear function boundaries and unit tests), and algorithmic complexity analysis within the Data Manipulation (SQL/Python) domain.
Data Manipulation (SQL/Python)Online Assessment - DRWHardMachine Learning EngineerTrain LinearSVC to beat a hidden baseline
Train LinearSVC to beat a hidden baseline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignOnline Assessment
Capital One
Citadel
Cohere
Coinbase
DRW