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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 211–240 of 782 results
- 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 a real-time recommendation system
This question evaluates a candidate's competency in designing large-scale, low-latency real-time recommendation systems, covering feature engineering and feature-store consistency, candidate generation and ranking architectures, cold-start handling, latency versus accuracy trade-offs, monitoring, and operational scalability.
ML System DesignOnsite - GoogleMediumMachine Learning EngineerDesign a watch-next video recommender and derive its losses and optimizer update rules
Design the watch-next recommendations shown while a user watches a video, from objectives and candidate generation to ranking, serving and evaluation. The interview probes modeling depth: exact loss functions for retrieval and multi-task ranking, the gradient at the output logit, and the update rules of the optimizers used to train each model.
ML System DesignOnsite - GoogleHardMachine Learning EngineerDesign feedback-driven recommender
Design feedback-driven recommender 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 DesignOnsite - GoogleHardMachine Learning EngineerDesign large-scale near-duplicate video detection
This question tests the ability to design an end-to-end pipeline for detecting near-duplicate videos at a massive scale, focusing on embedding generation, vector indexing, and trade-offs between precision and recall.
System DesignOnsiteComputer VisionEmbeddingsANN Search - 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 ML model fundamentals
Explain ML model 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 DesignOnsite - 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 - GoogleMediumMachine Learning EngineerExplain modeling challenges and fixes
This question evaluates end-to-end ML ownership and operational troubleshooting skills, including detection of data and modeling issues, trade-off analysis, and evidence-based impact measurement within the Machine Learning/MLOps domain.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerExplain ranking cold-start strategies
This question tests your ability to design strategies for surfacing new items and serving new users in a large-scale recommendation system while maintaining relevance and diversity.
Machine LearningTechnical ScreenRecommender SystemsCold StartEmbeddings - GoogleHardMachine Learning EngineerExplain transformer architecture and variants
This question evaluates a candidate's mastery of transformer architecture and related competencies such as attention mechanisms, positional encodings, encoder/decoder variants, computational complexity and scaling, stability and initialization practices, and adaptation of sequence models to molecular representations.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerGenerate values by weighted probabilities
This question evaluates understanding of probabilistic sampling, numerical stability, input validation, and algorithmic trade-offs for streaming weighted random draws, including generator determinism via seeding and methods such as CDF/binary-search versus alias for performance.
Statistics & MathOnsite - GoogleMediumMachine Learning EngineerImplement a Transformer Block with SwiGLU
This question evaluates implementation and reasoning skills for Transformer internals, testing competency in multi-head self-attention, SwiGLU gated feed-forward layers, residual connections, and layer normalization within the Machine Learning / deep learning domain.
Coding & AlgorithmsTechnical Screen - GoogleMediumMachine Learning EngineerImplement substring search and weighted sampling
This question tests the ability to implement a fast substring matching algorithm and a data structure that supports weighted random sampling from a dynamic collection of items.
Coding & AlgorithmsTechnical ScreenString MatchingKMPProbability - GoogleHardMachine Learning EngineerInterpret Language-Model Perplexity
Learn the exact likelihood basis of perplexity, correct evaluation details, tokenizer caveats, and limits as an LLM quality metric.
Machine LearningTechnical Screen - GoogleHardMachine Learning EngineerList regularization methods and trade-offs
This question evaluates competency in machine learning regularization and model generalization, covering familiarity with methods such as L1/L2, dropout, early stopping, data augmentation, label smoothing, mixup, normalization, Bayesian priors, and ensembling.
Machine LearningTechnical Screen - GoogleMediumMachine Learning EngineerMinimize Fence Painting Operations
This problem tests your ability to use a greedy approach or dynamic programming to find the minimum number of strokes required to paint a fence of varying heights.
Coding & AlgorithmsTechnical ScreenGreedyStackArrays - GoogleEasyMachine Learning EngineerPresent and Defend Your Research, Then Explain Your Research Interests
A research-focused interview question with no coding: introduce your own research, defend the details of a project under the interviewer's follow-up questions, and explain which research directions interest you. It tests problem framing, ownership of contributions, rigor in evaluation and fit with the team's work.
Behavioral & LeadershipTechnical Screen - GoogleHardMachine Learning EngineerSolve several streaming, DAG, and DP tasks
Implement an ML pipeline optimization engine that calculates critical path latency and peak parallelism for a task DAG, and optimizes streaming checkpoint placement under sliding-window constraints using dynamic programming.
Coding & AlgorithmsOnsiteDAGDynamic ProgrammingSliding Window - HackerRankMediumMachine Learning EngineerBuild A Purchase-Intent Prediction Notebook
Prepare for a machine learning take-home notebook that predicts purchase intent from session features and submits binary outcomes. The prompt focuses on data cleaning, missing values, categorical encoding, model pipelines, weighted F1, threshold tuning, and robust submissions.
Machine LearningOnline Assessment - HackerRankMediumMachine Learning EngineerCount Good Subsequences With Equal Character Frequencies
Practice a combinatorics coding problem that counts good subsequences where every selected character has the same frequency. The prompt targets frequency counting, combinations, modular arithmetic, and grouping subsequences by common selected count.
Coding & AlgorithmsOnline Assessment - HarveyMediumMachine Learning EngineerImplement a Spreadsheet With Formulas
This problem tests your ability to model cell dependencies using a directed graph, handle topological sorting for re-evaluation, and identify circular references.
Coding & AlgorithmsTechnical ScreenGraphsRecursionDFS
Google
Harvey