ML System Design interview questions

Showing 91–120 of 332 results

• Design Harmful Content DetectionMedium

  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.

  DatabricksMachine Learning EngineerML System DesignOnsite
• Design Harmful Content and OOM DetectionMedium

  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.

  DatabricksMachine Learning EngineerML System DesignOnsite
• Design Retrieval for a Coding Agent: What to Index, Handling Noise, and MetricsMedium

  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.

  DatabricksSoftware EngineerML System DesignOnsite
• Detect Harmful Content in LLM Prompts and ResponsesMedium

  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.

  DatabricksMachine Learning EngineerML System DesignOnsite
• Retrieve and Rerank Tables and Notebooks for a Data-Aware Coding AgentMedium

  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.

  DatabricksMachine Learning EngineerML System DesignOnsite
• Design an LLM Agent System That Automatically Resolves Jira Tickets and Opens Pull RequestsMedium

  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.

  DatadogSoftware EngineerML System DesignTechnical Screen
• Design an image detection systemHard

  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.

  DatadogSoftware EngineerML System DesignTechnical Screen
• Design a Scalable Audio AI Processing PlatformMedium

  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.

  DavidaiSoftware EngineerML System DesignTechnical Screen
• Design a Real-Time AI Voice SystemMedium

  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.

  DecagonSoftware EngineerML System DesignTechnical Screen
• Design a Single-Domain ChatbotMedium

  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.

  DecagonSoftware EngineerML System DesignOnsite
• Design a personalized recommendation systemHard

  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.

  DisneySoftware EngineerML System DesignTechnical Screen
• Design Streaming Speech and Multi-Model Chat SystemsMedium

  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.

  DistylMachine Learning EngineerML System DesignOnsite
• Design a Grounded LLM Assistant for Case PreparationHard

  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.

  DistylSoftware EngineerML System DesignTechnical Screen
• Design a Store Recommendation System and Explain ML Trade-offsMedium

  Design a personalized store recommendation feed for an on-demand delivery platform and evaluate key machine learning modeling trade-offs.

  DoorDashSoftware EngineerML System DesignOnsite
• Discuss ML infrastructure fundamentalsHard

  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.

  DoorDashMachine Learning EngineerML System DesignTechnical Screen
• Data quality, lineage, monitoring and backfill design for an ML feature storeMedium

  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.

  FaireMachine Learning EngineerML System DesignOnsite
• Design a real-time model inference platform: deployment, scaling and monitoringMedium

  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.

  FaireMachine Learning EngineerML System DesignOnsite
• Design Feed Ranking and HTML GenerationMedium

  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.

  FigmaMachine Learning EngineerML System DesignOnsite
• Design an Evaluation Platform for a Design-Editing AgentMedium

  Design an automated, multimodal evaluation platform to benchmark and monitor a canvas-based design-editing agent across deterministic property updates and subjective creative directives.

  FigmaSoftware EngineerML System DesignOnsite
• Design a Multimodal Training Data PipelineEasy

  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.

  FigureSoftware EngineerML System DesignTechnical Screen
• Recommend Connections Through Mutual Friends and Evaluate the RankingMedium

  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.

  FireworksMachine Learning EngineerML System DesignOnsite
• Improve Trust in a RAG SystemMedium

  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.

  Freddie MacMachine Learning EngineerML System DesignTechnical Screen
• Design an Agentic Ticket Support SystemMedium

  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.

  GigaSoftware EngineerML System DesignOnsite
• Design a Text-to-Image Retrieval SystemMedium

  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.

  Gimlet LabsMember of Technical Staff, ML SystemsML System DesignOnsite
• Design an Offline Inference Platform with Hardware-Aware RoutingMedium

  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.

  Gimlet LabsMember of Technical Staff, ML SystemsML System DesignOnsite
• Design Employee-to-Employee DistanceMedium

  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.

  GleanMachine Learning EngineerML System DesignOnsite
• Design a Work-Together Distance Between Any Two Employees of a CompanyMedium

  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.

  GleanMachine Learning EngineerML System DesignOnsite
• Explain an AI Agent You Built: Design, Evaluation, and GovernanceMedium

  Practice explaining an AI-agent project through design ownership, task evaluation, hallucination controls and data governance.

  GofundmeData ScientistML System DesignOnsite
• Choose Fast or Cheap ModelsMedium

  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.

  GoogleSoftware EngineerML System DesignTechnical Screen
• Design ML system for self-driving perceptionMedium

  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.

  GoogleMachine Learning EngineerML System DesignTechnical Screen