practice a little every day, paws crossed ♡
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 241–270 of 381 results
- PinterestHardMachine Learning EngineerPrevent Vanishing Gradients in Deep Networks
Tests a candidate's mathematical understanding of the vanishing gradient problem in deep neural networks and their ability to evaluate architectural mitigations including weight initialization, activation functions, residual connections, and normalization layers.
Machine LearningTechnical ScreenDeep LearningOptimizationNeural Networks - PinterestHardMachine Learning EngineerSolve a 9x9 Sudoku puzzle
This question evaluates algorithmic problem-solving skills focused on constraint satisfaction, state-space search, and implementation correctness for combinatorial puzzles.
Coding & AlgorithmsTechnical Screen - PlaidMediumMachine Learning EngineerDesign a Fraud Detection System
This question evaluates competency in ML system design for fraud detection within fintech data-connectivity platforms, testing real-time scoring, feature engineering (including relational/graph signals), label strategy, model training and serving, and operational concerns such as latency, privacy, monitoring, and robustness.
ML System DesignTechnical Screen - Point72MediumMachine Learning EngineerDesign Features for Residual Volatility
This problem tests the ability to decompose financial asset returns into systematic and idiosyncratic components and design features to capture residual volatility.
Machine LearningTechnical ScreenTime-SeriesFinanceFeature Engineering - Point72MediumMachine Learning EngineerFind the Smallest String After One Decrement
This question evaluates string manipulation, lexicographic ordering, and reasoning about local character transformations including wrap-around behavior.
Coding & AlgorithmsTechnical Screen - RbcroyalbankMediumMachine Learning EngineerNavigate Conflict, Compromise, and a Revealed Weakness
This set of behavioral prompts evaluates a candidate's ability to navigate professional friction, demonstrate self-awareness regarding technical limitations, and exercise judgment in collaborative software development.
Behavioral & LeadershipTechnical ScreenBehavioralConflict ResolutionLeadership - RbcroyalbankMediumMachine Learning EngineerProcess Operations for an In-Memory Key-Value Store
This problem tests proficiency with hash map operations and handling data retrieval edge cases within an in-memory data store.
Coding & AlgorithmsTechnical ScreenHash MapData StructuresAlgorithms - RedditMediumMachine Learning EngineerBuild and evaluate click prediction models
This question evaluates skills in supervised probabilistic modeling for click-through rate prediction, including model selection, calibration, evaluation metric choice, cross-validation, hyperparameter tuning, and production concerns like serving and monitoring.
Machine LearningTechnical Screen - RedditMediumMachine Learning EngineerDesign Comment Prediction Ranking System
This question assesses the ability to design an end-to-end ML pipeline for ranking comments by predicted user engagement probability, balancing accuracy, latency, and system scalability.
ML System DesignTechnical ScreenRankingRecommendation SystemsFeature Engineering - RedditEasyMachine Learning EngineerDesign a Tennis Scoring System
This problem tests your ability to model state transitions, encapsulate logic using object-oriented principles, and implement conditional rule-based scoring in a competitive game.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignState MachineLogic - RedditHardMachine Learning EngineerExplain and Reduce Position Bias in Ranking
Assess understanding of position bias in click-based ranking systems, including formalizing the examination hypothesis, estimating observation propensities, training with Inverse Propensity Scoring (IPS), and designing counterfactual evaluation metrics.
Machine LearningTechnical ScreenRankingRecommendationPosition Bias - RedditMediumMachine Learning EngineerImplement a simplified memcached server
This problem tests your ability to design a thread-safe, in-memory key-value store and implement basic command parsing for an asynchronous network service.
Coding & AlgorithmsTechnical ScreenConcurrencyData StructuresCaching - RobloxHardMachine Learning EngineerDesign a real-time game recommendation system
Design a real-time game recommendation system 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 DesignTechnical Screen - RobloxEasyMachine Learning EngineerExtend counter to per-client rate limiting
This question evaluates competency in designing per-client, time-windowed rate limiting using efficient data structures for time-based eviction and counting within the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - RobloxEasyMachine Learning EngineerFind the Most Frequent Log Call
This question evaluates a candidate's ability to perform frequency analysis on sequences, demonstrating competency with data structures for counting occurrences and with algorithmic time and space complexity considerations.
Coding & AlgorithmsTechnical Screen - RobloxEasyMachine Learning EngineerImplement recent-requests counter
This question evaluates a candidate's ability to design efficient in-memory data structures and manage time-windowed event counting, testing algorithmic reasoning and performance optimization for high-throughput services.
Coding & AlgorithmsTechnical Screen - RobloxMediumMachine Learning EngineerLevel-Ordered Dependency Build Order
This problem tests your ability to perform a Kahn’s algorithm-based topological sort while grouping tasks into parallelizable layers, ensuring cycle detection and dependency validation.
Coding & AlgorithmsTechnical ScreenGraphBFSTopological Sort - RunwayHardMachine Learning EngineerDesign a scalable video search system
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 Design a scalable video search system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - RunwayHardMachine Learning EngineerImplement n-gram model and select n
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Implement n-gram model and select n states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - SamsungMediumMachine Learning EngineerValidate palindrome with constraints
This question evaluates a candidate's ability to implement string-processing algorithms and two-pointer techniques under O(n) time and O(1) extra space constraints, including character filtering, case normalization, edge-case handling, and algorithmic complexity analysis.
Coding & AlgorithmsTechnical Screen - Scale AIMediumMachine Learning EngineerDebug ML pipeline and build text parser
This question evaluates practical machine learning engineering skills, including debugging unfamiliar codebases, parsing noisy text data, and identifying subtle defects such as train/test leakage or shape mismatches. It tests applied Python proficiency and systematic reasoning under time pressure, core competencies assessed in ML engineer technical interviews.
Data Manipulation (SQL/Python)Technical Screen - Scale AIHardMachine Learning EngineerExplain Transformers, attention, decoding, RL, and evaluation
This question evaluates understanding of transformer architectures, self- and cross-attention mechanisms, autoregressive decoding and sampling strategies, reinforcement learning–based fine-tuning (RLHF), evaluation methodologies, and training/inference optimization techniques.
Machine LearningTechnical Screen - Scale AIMediumMachine Learning EngineerImplement universal adversarial attack on GPT-2
Machine learning robustness prompt on safely evaluating universal adversarial prompts for GPT-2, covering threat modeling, differentiable optimization concepts, transferability, metrics, safety controls, and mitigation strategies without deployment-ready jailbreak details.
Machine LearningTechnical Screen - ShopifyMediumMachine Learning EngineerBuild a fraud detection model
Design an end-to-end machine learning system for real-time transaction fraud detection, covering label formulation under delayed feedback, feature design, threshold selection, and continuous monitoring.
Machine LearningTechnical Screenfraud-detectionmachine-learningsystem-design - ShopifyMediumMachine Learning EngineerBuild an Automatic Refund Decision System in a Pair-Programming Session
A pair-programming exercise to build an automatic refund system for an online store, combining light system design with working code across several incremental steps. It tests domain modeling, pluggable eligibility rules, preventing over-refunds and duplicate payouts on retries, and explaining design choices as you code.
Software Engineering FundamentalsTechnical Screen - ShopifyMediumMachine Learning EngineerExplain your career and flagship project
This question evaluates a candidate's ability to narrate career progression, demonstrate leadership and ownership, articulate technical decisions and trade-offs, and show production-grade machine learning engineering through a flagship-project deep dive that includes problem definition, business impact, ownership, and success metrics.
Behavioral & LeadershipTechnical Screen - ShopifyMediumMachine Learning EngineerImplement a Capacity-Bounded Cache
This question tests the ability to implement a Least Recently Used (LRU) cache, requiring efficient O(1) time complexity for both data retrieval and insertion through the combination of a hash map and a doubly linked list.
Coding & AlgorithmsTechnical ScreenHash MapLinked ListCache - ShopifyMediumMachine Learning EngineerSimulate a rover fleet
This problem tests your ability to manage state in a 2D grid simulation, coordinate multiple entities with movement logic, and enforce collision-avoidance constraints.
Coding & AlgorithmsTechnical ScreenSimulationGridCollision Detection - SiemensMediumMachine Learning EngineerBatch and Pad Variable-Length Vectors with Optional Final-Batch Dropping
Implement an ordered data pipeline function that partitions variable-length numerical sequences into batches, applies batch-local right padding, handles empty vectors, and optionally discards incomplete final batches.
Coding & AlgorithmsTechnical Screendata-pipelinesarraysmachine-learning - SiemensMediumMachine Learning EngineerCompare GRPO and PPO and Explain Sparse Versus Dense Rewards
This question evaluates your understanding of modern reinforcement learning post-training techniques for language models, focusing on GRPO versus standard PPO and the trade-offs between sparse outcome rewards and dense process supervision.
Machine LearningTechnical ScreenReinforcement LearningLLM Post-TrainingPPO
Pinterest
Plaid
Point72
Reddit
Roblox
Samsung
Scale AI
Shopify