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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 381 results
- DisneyMediumMachine Learning EngineerBuild Naive Bayes spam classifier with F1
This question evaluates understanding of probabilistic text classification with Naive Bayes, text preprocessing and feature extraction, and use of the F1 score for performance measurement in a binary spam detection task.
Machine LearningTechnical Screen - DoorDashMediumMachine Learning EngineerDesign personalized restaurant search and recommendations
This question tests the ability to design an end-to-end personalized restaurant recommendation system, covering feature engineering, retrieval-ranking architecture, and infrastructure trade-offs.
System DesignTechnical ScreenRecommendation SystemsRankingSearch - DoorDashHardMachine Learning EngineerDiscuss ML infrastructure fundamentals
Discuss ML infrastructure fundamentals 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 - DoorDashMediumMachine Learning EngineerMinimize batches and allocate riders by time
This problem tests your ability to optimize resource scheduling by grouping tasks with overlapping time windows into the minimum number of batches, subject to capacity constraints.
Coding & AlgorithmsTechnical ScreenGreedyIntervalsBin Packing - DoorDashEasyMachine Learning EngineerWalk Through an ML Project
This question assesses the candidate's ability to communicate a full-lifecycle machine learning project, focusing on problem scoping, technical execution, and business impact.
Behavioral & LeadershipTechnical ScreenML LifecycleProduct ThinkingModel Deployment - ExaHardMachine Learning EngineerDebug a PyTorch Contrastive Loss
Repair a PyTorch contrastive loss whose matrix multiplication contracts incompatible dimensions instead of producing pairwise scores. Validate aligned rank-two tensors and temperature, create the correct batch-by-batch logits, preserve gradients, and handle low-precision numerical stability.
Machine LearningTechnical Screen - ExaHardMachine Learning EngineerDesign a Streaming Percentile Estimator
Design a streaming percentile estimator without storing every observation, and define rank error precisely. Compare a deterministic single-stream summary with t-digest-style centroids across memory, update cost, duplicates, skew, tail accuracy, and distributed merge limitations.
Statistics & MathTechnical Screen - FigmaMediumMachine Learning EngineerClarify a BERT Keyphrase Span-Extraction Task
Implement and clarify the post-processing decoder for a BERT-based span-extraction model that maps token-level start and end logits back to non-overlapping character-level keyphrases.
Machine LearningTechnical Screennlpbertspan-extraction - FigmaHardMachine Learning EngineerEnd-to-End ML Project Deep Dive: Decisions, Evaluation Design and Career Goals
A screening question for an ML engineer role focused on post-training for agents. It asks you to present an end-to-end project you owned, defend its technical decisions and strategy, explain how you designed and ran its evaluation, and say why you are leaving your current company and where your career is heading.
Behavioral & LeadershipTechnical Screen - Freddie MacMediumMachine Learning EngineerDescribe a Machine Learning Project
This question evaluates a candidate's ability to lead end-to-end machine learning projects, emphasizing technical ownership, system design, data strategy, model selection, deployment, and stakeholder management.
Behavioral & LeadershipTechnical Screen - Freddie MacMediumMachine Learning EngineerImprove Trust in a RAG System
This question evaluates system-design competency for trustworthy retrieval-augmented generation, probing skills in retrieval and generation architecture, confidence scoring and calibration, evidence verification and fallback mechanisms, monitoring, privacy and compliance, human feedback integration, and adversarial robustness.
ML System DesignTechnical Screen - GatikHardMachine Learning EngineerDesign a YAML-Configured Point-Cloud Transformation Pipeline
Tests software engineering fundamentals and clean object-oriented design in Python by building an extensible, schema-validated point-cloud transformation pipeline with registry patterns and metadata tracking.
Software Engineering FundamentalsTechnical ScreenObject-Oriented DesignPoint CloudData Pipelines - GeneralmotorsHardMachine Learning EngineerCount Islands in a Grid
This problem tests your ability to navigate a 2D grid and identify distinct connected components using graph traversal techniques like Depth-First Search (DFS) or Breadth-First Search (BFS).
Coding & AlgorithmsTechnical ScreenGraphsDFSBFS - GleanMediumMachine Learning EngineerDetermine Reachability in Train Schedule
Solve train-schedule reachability with waiting and transfers using time-respecting graph search. The answer models earliest station arrival, preprocesses station visits, handles forward-only train routes, and explains transfer edge cases and complexity.
Coding & AlgorithmsTechnical Screen - GoogleMediumMachine Learning EngineerAnswer conflict, failure, and proud project questions
This question evaluates a Machine Learning Engineer's behavioral competencies such as conflict resolution, accountability, communication, leadership, and the ability to quantify impact by eliciting real examples of conflict, failure, and a proud project.
Behavioral & LeadershipTechnical Screen - GoogleHardMachine Learning EngineerCompare Language-Model Post-Training Methods
This question tests your conceptual depth in LLM alignment by asking you to differentiate between supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), direct preference optimization (DPO), and group-relative optimization (e.g., GRPO) based on their mathematical objectives and operational characteristics.
Machine LearningTechnical ScreenLLMAlignmentRLHF - GoogleHardMachine Learning EngineerConstruct connected crop layout and safe paths
This problem tests your ability to design a constructive grid layout that satisfies connectivity and size constraints, followed by pathfinding through dynamic or constrained environments.
Coding & AlgorithmsTechnical ScreenBacktrackingGraph TheoryBFS - GoogleMediumMachine Learning EngineerDescribe leadership under changing priorities
This question evaluates leadership, ownership, prioritization, communication, and adaptability skills within the Behavioral & Leadership domain for a Machine Learning Engineer role.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerDescribe your proudest project
This question evaluates leadership, communication, and product-focused machine learning engineering competencies, including end-to-end project ownership, technical decision-making, cross-functional collaboration, metrics-driven impact measurement, and risk management; it is categorized as Behavioral & Leadership within the Machine Learning Engineering domain. It is commonly asked to determine how candidates articulate trade-offs, quantify outcomes, justify technical choices, and demonstrate both conceptual understanding and practical application of ML systems, constraints, and tooling in real-world projects.
Behavioral & LeadershipTechnical Screen - GoogleMediumMachine Learning EngineerDesign ML system for self-driving perception
This question assesses the ability to architect a real-time perception system for autonomous vehicles, focusing on sensor integration, model optimization for edge deployment, and safety-critical failure handling.
ML System DesignTechnical ScreenComputer VisionSensor FusionEdge ML - GoogleMediumMachine Learning EngineerDesign a Product or Video Recommendation System
This question evaluates competency in designing large-scale recommendation systems, encompassing machine learning modeling, candidate generation and ranking, data engineering, online serving, feedback-driven learning, evaluation, and experimentation.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign a reaction-factor prediction system
This question evaluates competency in designing end-to-end machine learning systems for molecular pair regression tasks, including data quality and normalization, molecular representation and feature engineering, model selection, evaluation metrics, uncertainty estimation, and production considerations.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDesign multi-GPU matrix multiplication
This question evaluates proficiency in multi-GPU parallelism and system-level ML engineering, covering data partitioning, inter-GPU communication primitives, compute scheduling and overlap, memory layout and buffer reuse, numerical precision trade-offs, synchronization, scalability, and failure handling.
ML System DesignTechnical Screen - GoogleHardMachine Learning EngineerDetect and Prevent Reward Hacking
Assess the candidate's understanding of reward hacking (specification gaming) in reinforcement learning from human feedback (RLHF), focusing on diagnosing exploitation patterns, designing detection metrics, applying conservative policy updates, and implementing recovery safeguards.
Machine LearningTechnical ScreenRLHFReward ModelingLLM Alignment - GoogleMediumMachine Learning EngineerExplain GRPO-style training for diffusion models
This question tests your conceptual understanding of applying Group Relative Policy Optimization (GRPO) to diffusion models, focusing on how reinforcement learning can be used to steer image generation toward specific objective functions without explicit gradient feedback from the decoder.
Machine LearningTechnical ScreenDiffusion ModelsReinforcement LearningGRPO - GoogleHardMachine Learning EngineerExplain Importance Sampling in LLM Training
Tests understanding of importance sampling in modern LLM fine-tuning, focusing on sequence-level likelihood ratios, exponential variance growth, support conditions, and practical mitigations like clipping and diagnostics.
Machine LearningTechnical Screenreinforcement-learningllm-trainingimportance-sampling - GoogleHardMachine Learning EngineerExplain KL Divergence in Language Models
Review KL divergence from its probability definition through directionality, support mismatch, sample estimates, and LLM regularization.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerExplain LLM lifecycle and trade-offs
This question evaluates a candidate's understanding of the end-to-end lifecycle of large language models, covering training data collection and filtering, pretraining objectives, transformer architecture, post-training methods like supervised fine-tuning and preference optimization, and common model or training variants.
Machine LearningTechnical Screen - GoogleHardMachine Learning EngineerExplain Pipeline Parallelism in GRPO Training
Design and analyze an efficient pipeline parallel training architecture for Group Relative Policy Optimization (GRPO) on large reasoning models. Tests deep understanding of pipeline scheduling, microbatching, distributed advantage normalization, and policy staleness trade-offs.
ML System DesignTechnical ScreenPipeline ParallelismRLHF / GRPODistributed Training - GoogleHardMachine Learning EngineerExplain VAEs, ELBO, KL, and Reparameterization
Derive and explain the theoretical foundations of Variational Autoencoders (VAEs), linking the Evidence Lower Bound (ELBO) to network architecture, gradient estimation via the reparameterization trick, and posterior collapse.
Machine LearningTechnical ScreenVariational AutoencodersGenerative ModelsDeep Learning
Disney
DoorDash
Figma
Freddie Mac
Glean
Google