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Software 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 31–60 of 121 results
- BasetenHardSoftware EngineerDesign an LLM Inference Serving System
Design an end-to-end, high-performance LLM serving platform that manages request admission, continuous batching, KV cache memory, token streaming, and GPU fleet autoscaling under tight latency SLAs.
ML System DesignTechnical Screenml-system-designllm-servingdistributed-systems - ByteDanceMediumSoftware EngineerDesign a Content Moderation Platform
This problem evaluates your ability to design a scalable, multi-modal content moderation system that filters user-uploaded media using both automated ML inferences and human review workflows.
ML System DesignOnsiteSystem DesignMLOpsScalability - ByteDanceMediumSoftware EngineerDesign a RAG-Based Agent System
This question tests the ability to architect a scalable Retrieval-Augmented Generation (RAG) system that handles document indexing, retrieval, and multi-step reasoning while maintaining low latency and data freshness.
ML System DesignTechnical ScreenRAGLLMSystem Design - ByteDanceMediumSoftware EngineerDesign a Short-Video Recommendation System
This problem tests the ability to design a multi-stage recommendation pipeline that balances real-time personalization, system latency, and content diversity.
ML System DesignOnsiteSystem DesignRecommendation SystemsScalability - CitadelHardSoftware EngineerBuild models for housing and wind power prediction
This question evaluates ML system design and applied modeling competencies, including data assumptions, preprocessing, feature engineering, model selection, validation strategies, temporal leakage handling, and end-to-end pipeline construction for a binary housing-affordability classifier and a time-series regression for wind-farm power prediction.
ML System DesignOnline Assessment - CrestaMediumSoftware EngineerDesign an Evaluation Platform for AI Agents Across Prompt and Model Changes
An ML system design question about a platform that lets teams evaluate AI agents and measure how a change of system prompt or model affects quality, tool use and response speed. It tests evaluation design, scoring with references and LLM judges, statistical comparison of versions and scalable execution.
ML System DesignOnsite - CriblMediumSoftware EngineerDesign an LLM Log Parsing Workflow
This question evaluates skills in ML-enabled log parsing, schema inference, structured data extraction, and designing scalable, reliable production workflows that combine probabilistic LLMs with deterministic parsers and operational engineering.
ML System DesignTechnical Screen - 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 - 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 - 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 - 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 - 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 - 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 - GoogleMediumSoftware EngineerDesign a Music Recommendation System with an LLM Agent
Design a large-scale music recommendation system combining traditional multi-stage ranking with an LLM agent for natural language intent parsing and contextual explanations.
ML System DesignTechnical ScreenRecommender SystemsLLM AgentsInformation Retrieval - GoogleMediumSoftware EngineerDesign an Agent for Internal Document Search
Design an enterprise search agent that provides grounded, cited answers over internal knowledge repositories while strictly enforcing access control lists (ACLs) and handling prompt injection.
ML System DesignTechnical ScreenRAGSearch SystemsLLM Agents - GoogleMediumSoftware EngineerDesign an LLM Agent for Manufacturing: Production Status, Work Orders and Quotes
Design an LLM-powered agent for a manufacturing business that checks production progress, opens work orders and gives customers price quotes. Probes tool design for reads and writes, how to stop the agent from inventing statuses or prices, and how to enforce user permissions outside the model.
ML System DesignTechnical Screen - GoogleHardSoftware EngineerDesign anomaly detection and response platform
This question evaluates a candidate's competency in designing end-to-end ML-driven systems that handle large-scale, low-latency OS snapshot ingestion, binary anomaly detection, decisioning and action orchestration with multi-tenant security, auditability, and failure rollback.
ML System DesignTechnical Screen - GrammarlyMediumSoftware EngineerBuild AI chat for spreadsheets
This question evaluates a candidate's ability to design ML-driven conversational interfaces and systems integration, emphasizing LLM orchestration, tool schema and API design, authorization and validation, ambiguity handling, atomic multi-step execution, safety against prompt injection, and operational concerns like latency, cost, scaling, and observability. It is commonly asked to assess architectural thinking for integrating language models with controlled tool-calling and safe execution; category: ML System Design; level of abstraction: system-level design requiring both conceptual understanding and practical application.
ML System DesignOnsite - HarveyHardSoftware EngineerDesign Retrieval-Augmented Answers from a Document Vault
Design an enterprise-grade retrieval-augmented generation (RAG) service for confidential document vaults. The question tests multi-tenant access control, precise citation provenance, document versioning/freshness, retrieval quality, and hallucination/injection defenses.
ML System DesignOnsiteRAGAccess ControlInformation Retrieval - HarveyMediumSoftware EngineerDesign a Memo Q&A Agent for a Large Law Firm
This problem evaluates your ability to design a RAG-based Q&A system for sensitive legal documents, focusing on multi-stage retrieval, hallucination prevention, and data privacy.
ML System DesignOnsiteRAGLLMSystem Design - HarveyHardSoftware EngineerDesign a RAG Q&A Agent over Law Firm Legal Memos
Design an end-to-end Retrieval-Augmented Generation (RAG) system for querying confidential, complex legal memos. This evaluates domain-specific parsing, hybrid retrieval with fine-grained access control, and grounded generation with strict citation fidelity.
ML System DesignOnsiteRAGVector SearchInformation Retrieval - HarveyMediumSoftware EngineerDesign an LLM Agent System That Revises a Contract to Comply with Dozens of Rules
Design an LLM agent system that revises a given contract so it complies with several dozen given legal rules. It tests decomposing work across model calls, representing edits so they can be traced and merged, resolving overlapping or conflicting changes, per-rule verification, and cost and latency trade-offs.
ML System DesignOnsite - HarveyMediumSoftware EngineerDesign and Evaluate a Retrieval-Augmented Generation Pipeline
Design a RAG pipeline with document chunking, retrieval recall evaluation, grounded answers, permission-aware context, and versioned index updates.
ML System DesignOnsite
Baseten
Citadel
Cresta
Databricks
Datadog
Decagon
Disney
DoorDash
Figma
Google
Grammarly
Harvey