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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 1–30 of 57 results
- AdobeHardSoftware EngineerDesign a multimodal embedding service
This question evaluates a candidate's competency in ML system design for building scalable, multi‑tenant, privacy‑sensitive multimodal embedding pipelines, covering ingestion and idempotency, modality-specific preprocessing, model selection and fusion, throughput engineering, storage and versioning, data hygiene, and operational concerns.
ML System DesignTechnical Screen - AdobeHardSoftware EngineerDesign file-embedding storage system
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 Design file-embedding storage system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - AmazonEasySoftware EngineerDesign a Reliable AI Chatbot Architecture
Tests candidate's ability to design an enterprise-grade Retrieval-Augmented Generation (RAG) chatbot system featuring fine-grained authorization, hallucination guardrails, and low-latency response serving.
ML System DesignTechnical ScreenRAGLLMInformation Retrieval - AmazonHardSoftware EngineerDesign a fraud detection system
This problem evaluates your ability to design a scalable, low-latency fraud detection system for an e-commerce platform, focusing on handling class imbalance, feature engineering, and the challenges of delayed feedback loops.
ML System DesignTechnical ScreenFraud DetectionScalabilityFeature Store - AmazonHardSoftware EngineerDesign an e-commerce recommendation system
This question evaluates expertise in large-scale machine learning system design, covering competencies in recommendation algorithms, candidate generation and ranking, feature engineering and stores, low-latency serving, and operational reliability; it is categorized in the ML system design domain and requires both conceptual understanding and practical application. It is commonly asked to assess the ability to balance personalization and business metrics (CTR, CVR, revenue), address scalability and latency targets, and manage data quality, cold-start, exploration–exploitation, A/B testing, bias/fairness, and monitoring trade-offs in production.
ML System DesignTechnical Screen - AmazonHardSoftware EngineerDesign an email spam detection system
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 Design an email spam detection system states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - AmazonHardSoftware EngineerOptimize attention for long sequences
Optimize attention for long sequences 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 - AnthropicMediumSoftware EngineerDesign a Low-Latency GPU Inference Service
This problem tests the ability to design a high-throughput, low-latency inference architecture for large language models, focusing on hardware utilization and bottleneck mitigation.
ML System DesignTechnical ScreenMLOpsLLMDistributed Systems - AnthropicMediumSoftware EngineerDesign an LLM-based binary classifier
This question evaluates knowledge of LLM-based binary classification, probabilistic scoring and numerical stability (including log-prob handling and log-sum-exp), prompt construction, batching and sequence-level aggregation, calibration and evaluation metrics, plus analysis of failure modes and inference-time complexity.
ML System DesignTechnical Screen - AnthropicMediumSoftware EngineerReview and Scale an Inference Service Design
Tests the ability to architect a high-throughput ML inference service under strict latency SLAs, including dynamic batching, hardware capacity estimation, admission control, and multi-model routing.
ML System DesignTechnical Screenml-system-designdistributed-systemsgpu-inference - AnthropicHardSoftware EngineerRoute and Batch Inference with Eight GPUs
Design an inference routing, dynamic batching, and scheduling system across a fixed pool of eight 80GB GPUs serving both large and small language models under strict latency and memory constraints.
ML System DesignTechnical Screenml-systemsllm-inferencegpu-scheduling - AppleHardSoftware EngineerDesign a 200-Terabyte Media Migration and Training Input Pipeline
This problem tests your ability to design a high-throughput data ingestion pipeline, focusing on network bottlenecks, storage strategies, and synchronization between on-premises assets and cloud-based training clusters.
ML System DesignTechnical ScreenSystem DesignData EngineeringMLOps - 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 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 - 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 - 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 - 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 - 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 - 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 - HebbiaMediumSoftware EngineerDesign Hebbia Chat for SEC Filings
This problem tests your ability to design a retrieval-augmented generation (RAG) system specialized for financial documents, focusing on multi-agent orchestration, citations, and latency management.
ML System DesignTechnical ScreenRAGLLMSystem Design - Hive.AiEasySoftware EngineerDesign a Synchronous Text Classification API
This question tests the ability to architect a robust, scalable, and maintainable synchronous ML inference service, focusing on operational reliability and multi-tenant management.
ML System DesignTechnical ScreenSystem DesignAPI DesignMLOps - IntuitEasySoftware EngineerDesign Premium Product Recommendations
This question tests the ability to design an end-to-end recommendation architecture, focusing on the trade-offs between relevance, latency, and data privacy in a financial context.
ML System DesignTechnical ScreenSystem DesignRecommendation SystemsML Ops
Adobe
Amazon
Anthropic
Apple
Baseten
Datadog
Decagon
Disney
Google
Intuit