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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
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- 11
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Showing 61–90 of 178 results
- Jane StreetHardMachine Learning EngineerPredict future time-series values
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 Predict future time-series values states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - LilaMediumMachine Learning EngineerDesign a DNA-sequence optimization loop
This problem tests your ability to design a closed-loop active learning system for protein or DNA sequence engineering, focusing on the iterative cycle of sequence generation, uncertainty estimation, and experimental validation.
ML System DesignOnsiteActive LearningGenerative ModelsBioinformatics - LinkedInMediumMachine Learning EngineerDesign LinkedIn Learning course recommendations
The question evaluates proficiency in end-to-end machine learning system design, covering feature engineering from job post text, candidate generation and ranking, model training and labeling, evaluation metrics, and production concerns such as cold start, bias, and feedback loops.
ML System DesignOnsite - LinkedInHardMachine Learning EngineerDesign a Model That Predicts Whether a Member Clicks a Displayed Job
An ML system design question: predict whether a member will click a job displayed to them, using member features, job content and interaction history. It tests defining the supervision signal and training data, choosing a loss, preventing data leakage, building member and job embeddings, and laying out the training and serving components.
ML System DesignTechnical Screen - LinkedInMediumMachine Learning EngineerDesign a Personalized Feed and Compare Ranking Objectives
Design an end-to-end personalized professional feed system and compare pointwise, pairwise, and listwise learning-to-rank formulations across offline training, online serving, and business metrics.
ML System DesignTechnical Screenml-system-designrecommendation-systemslearning-to-rank - LinkedInMediumMachine Learning EngineerDesign a Skills inference system
This question assesses the ability to build an end-to-end ML pipeline for extracting and normalizing professional skills from unstructured user-generated text, such as job descriptions or member profiles.
ML System DesignOnsiteNLPEntity LinkingRecommendation Systems - LinkedInMediumMachine Learning EngineerDesign a system for LinkedIn Skills
This question evaluates a machine learning engineer's ability to design an end-to-end ML system for skill inference and recommendation on a professional networking platform, testing competencies in data sourcing and labeling, feature engineering, model selection (including LLM integration), online serving, monitoring, and quality control.
ML System DesignOnsite - LinkedInMediumMachine Learning EngineerDesign an ML Forecasting and Pacing System for Ad Bidding
Design an ad forecasting and budget pacing architecture that combines predictive ML with deterministic budget guardrails to optimize campaign delivery across non-stationary traffic.
ML System DesignTechnical Screenml-system-designad-techtime-series - MercorMediumMachine Learning EngineerBuild a Candidate Search System
Design an end-to-end talent search and matching engine that combines semantic vector retrieval with structured filtering to handle unstructured resumes, ambiguous queries, and strict hiring constraints.
ML System DesignOnsiteml system designinformation retrievalnlp - MercorMediumMachine Learning EngineerBuild an Ollama Eval Harness
This question evaluates a candidate's ability to design and implement a lightweight evaluation harness for a locally hosted language model, testing competencies in ML system design, service integration, metrics instrumentation, reliability engineering (timeouts, retries), concurrency control, configuration management, and extensibility.
ML System DesignTechnical Screen - MercorMediumMachine Learning EngineerDesign Candidate Search And LLM Evaluation Tasks
Prepare for an MLE system design interview that combines candidate search, ranking, comparator reliability, and language-model fundamentals. The prompt covers retrieval, ranking feedback loops, top-k reasoning, transformer concepts, and decoding trade-offs without requiring proprietary hiring data.
ML System DesignOnsite - MercorMediumMachine Learning EngineerDesign Work Orchestration for Machine-Learning Data Pipelines
Design a resilient, multi-tenant orchestration engine tailored for machine learning pipelines that manages versioned DAGs, heterogeneous compute routing, data quality gates, and atomic dataset publication.
ML System DesignOnsiteML InfrastructureSystem DesignWorkflow Orchestration - MetaHardMachine Learning EngineerArchitect an asynchronous RL post-training system
This question evaluates a candidate's ability to design an asynchronous reinforcement-learning post-training system for a production chat LLM, testing competencies in ML system architecture, distributed training and serving separation, streaming data engineering, reward modeling and credit assignment, safety/compliance, and deployment/operations.
ML System DesignOnsite - MetaHardMachine Learning EngineerDesign Nearby and Notification Ranking
This question evaluates competency in designing large-scale machine learning systems for real-time recommendations and notification ranking, covering candidate generation, ranking, feature engineering, online serving, latency management, and multi-objective trade-offs; it is commonly asked to assess the ability to translate product objectives into system architecture and measurable evaluation metrics while addressing freshness, cold-start, moving users, and notification fatigue. The domain is ML system design—recommender systems, ranking, and online serving—and the level spans both high-level conceptual architecture and practical application concerns such as serving latency, data freshness, training labels, and evaluation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign Place Recommendation System
This question evaluates machine learning system design skills specific to recommender systems, including competencies in feature engineering, candidate generation and ranking, data engineering, online serving, latency and real-time context handling, evaluation metrics, and fairness/bias mitigation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Large-Scale Personalized Recommendation System End to End
An ML system design question asking you to design a large-scale personalized recommendation system end to end, from candidate retrieval and multi-stage ranking to training data, evaluation, serving and monitoring. It tests problem framing, latency budgeting, cold start, feedback loops and online experimentation.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a Real-Time Question-Answering Chatbot: Training Data, RL Tasks and Rewards
An AI design question about a chatbot that answers user questions in real time. It tests the low-latency serving and retrieval path, training data collection for fine-tuning and preference learning, reinforcement-learning task and reward design, and the ability to name reward weaknesses and mitigate them.
ML System DesignTechnical Screen - MetaMediumMachine Learning EngineerDesign a Short-Video Recommendation System
This question tests your ability to design a multi-stage recommendation pipeline for a high-traffic short-video platform, focusing on candidate retrieval, ranking models, and system scalability.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityRanking - MetaMediumMachine Learning EngineerDesign a newsfeed dislike model
This problem tests the ability to design an end-to-end ML system for predicting user dissatisfaction, focusing on label definition, feature engineering, and the integration of negative signals into a ranking pipeline.
ML System DesignOnsiteRecommender SystemsRankingImbalanced Learning - MetaHardMachine Learning EngineerDesign a scalable MoE pretraining pipeline
This question evaluates expertise in large-scale ML system design and distributed training for Mixture-of-Experts (MoE) models, covering competencies in model architecture, parallelism and communication strategies, memory and optimizer sharding, data curation and tokenization, monitoring, checkpointing, and fault tolerance; it is categorized under ML System Design and targets practical application with systems-level architectural reasoning rather than purely theoretical concepts. It is commonly asked to evaluate the ability to balance throughput, stability, and cost when scaling pretraining to hundreds of GPUs, reason about routing and capacity trade-offs, and design production-grade pipelines that can extend to larger clusters.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign a weapon-sale ad detection system
This question tests the ability to architect an end-to-end multimodal classification pipeline for detecting policy-violating content at scale while balancing precision, recall, and operational latency.
ML System DesignOnsiteMultimodal LearningContent ModerationClassification - MetaHardMachine Learning EngineerDesign an LLM-Based Coding Assistant
Design an end-to-end LLM-powered coding assistant supporting inline code completion and repository-level chat under strict latency constraints.
ML System DesignOnsiteLLMInference ServingCode Generation - MetaHardMachine Learning EngineerDesign an LLM-Based Conversational Assistant (Chatbot)
Design an end-to-end conversational AI assistant powered by large language models, covering post-training alignment, retrieval/tool orchestration, low-latency serving, and safety guardrails at consumer scale.
ML System DesignOnsiteLLMRAGModel Serving - MetaMediumMachine Learning EngineerDesign an image copyright-violation detection system
This problem evaluates your ability to design a scalable, end-to-end computer vision pipeline capable of identifying copyrighted content within user-uploaded images, including scenarios involving cropping, filters, and collages.
ML System DesignOnsiteComputer VisionEmbeddingsSimilarity Search - MetaMediumMachine Learning EngineerDesign comment ranking for a news feed
This question tests the ability to design an end-to-end machine learning system for ranking comments, covering feature engineering, model architecture, and the trade-offs between engagement, quality, and latency.
ML System DesignOnsiteRankingRecommendation SystemsMulti-task Learning - MetaHardMachine Learning EngineerDesign image and multimodal generation systems
This question evaluates a candidate's competence in designing end-to-end image and multimodal generation systems, covering data collection and curation, model architecture and conditioning choices, training objectives, safety and content filtering, evaluation metrics, deployment, monitoring, and critical analysis of relevant research.
ML System DesignTechnical Screen - MetaHardMachine Learning EngineerDesign nearby place recommendations
This question evaluates skills in designing real-time, large-scale machine learning recommender systems that combine geospatial candidate generation, feature engineering, ranking models, personalization, privacy controls, fraud detection, and operational constraints like latency and throughput.
ML System DesignOnsite - MetaMediumMachine Learning EngineerDesign weapon-selling ad detection from posts
This question tests the ability to design an end-to-end multimodal classification system for detecting policy-violating content (weapon sales) while balancing latency, precision, and robustness against adversarial evasion.
ML System DesignOnsiteComputer VisionNLPMultimodal Learning - MetaMediumMachine Learning EngineerPrevent Private Code Leakage in Coding Agents
This problem tests the ability to design an ML-driven safeguard system that prevents private codebase exposure during the training, fine-tuning, and inference phases of a coding agent.
ML System DesignTechnical ScreenMLOpsSecurityPrivacy - MicrosoftMediumMachine Learning EngineerCalibrate LLM output to match Word formatting
This question tests the ability to design an end-to-end system for enforcing strict structured formatting (e.g., Docx/Markdown) on LLM-generated text, focusing on constraint enforcement and reliability.
ML System DesignOnsiteLLMSystem DesignConstrained Decoding
Jane Street
LinkedIn
Mercor
Meta
Microsoft