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ML System Design 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 91–120 of 332 results
- DatabricksMediumMachine Learning EngineerDesign Harmful Content Detection
This problem tests the ability to design an end-to-end machine learning system for multimodal harmful content detection at scale, focusing on pipeline architecture, latency, and data governance.
ML System DesignOnsiteSystem DesignMultimodalScalability - DatabricksMediumMachine Learning EngineerDesign Harmful Content and OOM Detection
This question tests the ability to design an end-to-end ML system for real-time safety, covering taxonomy, data strategies, model architecture, and operational monitoring.
ML System DesignOnsiteSystem DesignMLOpsSafety - DatabricksMediumSoftware EngineerDesign Retrieval for a Coding Agent: What to Index, Handling Noise, and Metrics
Design the retrieval layer that feeds code and documentation to an LLM coding agent, deciding what to index, how to split and rank it, how to handle noise, and which metrics prove it works. The question tests code-aware indexing, hybrid lexical and dense retrieval, reranking, freshness, and offline and end-to-end evaluation design.
ML System DesignOnsite - DatabricksMediumMachine Learning EngineerDetect Harmful Content in LLM Prompts and Responses
Design a machine learning system that detects harmful content in prompts sent to, and responses produced by, a large language model. Tests policy-driven framing, labeling and adversarial data, layered classifiers under latency limits, per-category evaluation, streaming checks and monitoring.
ML System DesignOnsite - DatabricksMediumMachine Learning EngineerRetrieve and Rerank Tables and Notebooks for a Data-Aware Coding Agent
Design the retrieval system that lets an internal coding agent find the right tables and notebooks to answer data questions. Tests what to index for each asset type, hybrid retrieval plus reranking, handling noisy or stale assets, access control, and metric design from retrieval to final answers.
ML System DesignOnsite - DatadogMediumSoftware EngineerDesign an LLM Agent System That Automatically Resolves Jira Tickets and Opens Pull Requests
Design an autonomous agent pipeline that ingests Jira bug reports, grounds itself in a multi-repo codebase via code search and retrieval, iterates on a fix in a sandboxed runtime, and opens a validated pull request.
ML System DesignTechnical ScreenLLM AgentsCode GenerationRAG - DatadogHardSoftware EngineerDesign an image detection system
This question evaluates a candidate's competence in designing scalable, reliable end-to-end machine learning systems for image object detection, including ingestion, preprocessing, model serving, data and version management, monitoring, and operational concerns.
ML System DesignTechnical Screen - DavidaiMediumSoftware EngineerDesign a Scalable Audio AI Processing Platform
Tests the candidate's ability to design a resilient, distributed ML platform for audio processing that supports both interactive and batch workloads, efficient GPU scheduling, and granular failure recovery.
ML System DesignTechnical Screenml-system-designdistributed-systemsgpu-scheduling - DecagonMediumSoftware EngineerDesign a Real-Time AI Voice System
This problem evaluates your ability to design a low-latency, real-time conversational AI system, focusing on component orchestration and performance trade-offs in speech processing pipelines.
ML System DesignTechnical ScreenSystem DesignReal-time SystemsStreaming - DecagonMediumSoftware EngineerDesign a Single-Domain Chatbot
This question evaluates a candidate's ability to design production-grade conversational AI systems, testing competencies in retrieval-augmented generation, document ingestion and indexing, multi-turn session management, prompt construction, model serving, grounding and citation, safety, access control, monitoring, and operational trade-offs.
ML System DesignOnsite - DisneyHardSoftware EngineerDesign a personalized recommendation system
This question evaluates a candidate's competency in ML system design and recommender systems engineering, covering data ingestion, feature stores, candidate generation, ranking, online serving, experimentation, and operational considerations.
ML System DesignTechnical Screen - DistylMediumMachine Learning EngineerDesign Streaming Speech and Multi-Model Chat Systems
A two-part system-design interview covering incremental multi-speaker transcription and a streaming multi-model chat service. Candidates must reason about ordering, revisions, backpressure, model fallback, durable conversation state, safety, and recovery.
ML System DesignOnsite - DistylHardSoftware EngineerDesign a Grounded LLM Assistant for Case Preparation
Design an enterprise-grade, retrieval-grounded LLM assistant for litigation teams to summarize discovery documents and draft case briefs with strict matter isolation, citation provenance, contradiction detection, and injection guardrails.
ML System DesignTechnical ScreenRAGLLM SafetyInformation Retrieval - DoorDashMediumSoftware EngineerDesign a Store Recommendation System and Explain ML Trade-offs
Design a personalized store recommendation feed for an on-demand delivery platform and evaluate key machine learning modeling trade-offs.
ML System DesignOnsiteRecommendation SystemsMulti-Task LearningRanking - 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 - FaireMediumMachine Learning EngineerData quality, lineage, monitoring and backfill design for an ML feature store
A discussion-based ML infrastructure design round on keeping the data behind models reliable and well governed. It covers defining and measuring availability, freshness, completeness, validity, consistency, distribution, compliance and lineage, monitoring a feature store, and planning a safe, correct data backfill.
ML System DesignOnsite - FaireMediumMachine Learning EngineerDesign a real-time model inference platform: deployment, scaling and monitoring
An ML system design question asking for a real-time model inference platform that teams use to serve trained models inside their applications' request paths. It tests safe model deployment and rollback, per-model autoscaling and batching, failure handling, and monitoring from infrastructure up to model quality.
ML System DesignOnsite - FigmaMediumMachine Learning EngineerDesign Feed Ranking and HTML Generation
This problem tests the design of a dual-purpose ML system: a personalized content feed ranker and an LLM-based component that translates user intent into rendered HTML widgets.
ML System DesignOnsiteRankingLLMRecommendation Systems - FigmaMediumSoftware EngineerDesign an Evaluation Platform for a Design-Editing Agent
Design an automated, multimodal evaluation platform to benchmark and monitor a canvas-based design-editing agent across deterministic property updates and subjective creative directives.
ML System DesignOnsiteLLM EvaluationAgentsMultimodal AI - FigureEasySoftware EngineerDesign a Multimodal Training Data Pipeline
This question evaluates a candidate's ability to design scalable, reliable backend systems for ingesting and processing multimodal training data, testing competencies in distributed system architecture, API and storage design, large-media handling, data validation, duplicate detection, quality control, observability, and awareness of data bias.
ML System DesignTechnical Screen - FireworksMediumMachine Learning EngineerRecommend Connections Through Mutual Friends and Evaluate the Ranking
Design a connection recommendation system based on mutual connections, define bounded graph affinity scores, and evaluate the ranking pipeline using metrics like NDCG with independent labels.
ML System DesignOnsiteGraph MLRecommender SystemsRanking - 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 - GigaMediumSoftware EngineerDesign an Agentic Ticket Support System
This question evaluates a candidate's ability to design ML-driven production systems that combine agentic AI, browser-based automation for services without an API, and external user-data APIs to produce personalized support responses.
ML System DesignOnsite - Gimlet LabsMediumMember of Technical Staff, ML SystemsDesign a Text-to-Image Retrieval System
Design permission-aware text-to-image retrieval with safe ingestion, multimodal embeddings, metadata filtering, approximate search, reranking, relevance evaluation, versioned updates, deletion, and duplicate control.
ML System DesignOnsite - Gimlet LabsMediumMember of Technical Staff, ML SystemsDesign an Offline Inference Platform with Hardware-Aware Routing
Design an offline large-model inference platform that routes prefill and decode across profiled hardware pools while balancing deadlines, fairness, batching, transfer cost, retries, and utilization.
ML System DesignOnsite - GleanMediumMachine Learning EngineerDesign Employee-to-Employee Distance
This question evaluates competencies in ML system design, similarity and representation modeling, feature and data engineering, scalable low-latency serving architectures, and privacy, fairness, and evaluation practices.
ML System DesignOnsite - GleanMediumMachine Learning EngineerDesign a Work-Together Distance Between Any Two Employees of a Company
Design a learned distance between any two employees of a company that reflects how much they actually work together. Tests ML framing without ground-truth labels, collaboration signals and their normalization, link-prediction training, evaluation, privacy and serving at company scale.
ML System DesignOnsite - GofundmeMediumData ScientistExplain an AI Agent You Built: Design, Evaluation, and Governance
Practice explaining an AI-agent project through design ownership, task evaluation, hallucination controls and data governance.
ML System DesignOnsite - GoogleMediumSoftware EngineerChoose Fast or Cheap Models
This question evaluates a candidate's competency in ML system design, focusing on balancing cost-versus-latency trade-offs for inference routing, operational metrics, workload segmentation, and reliability.
ML System DesignTechnical 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
Databricks
Datadog
Decagon
Disney
DoorDash
Faire
Figma
Freddie Mac
Glean
Google