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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 61–90 of 121 results
- 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 - IntuitMediumSoftware EngineerAdd an LLM-backed recommendation API to an existing service with RAG and prompting
Add an LLM-powered recommendation endpoint to an existing backend service. Design the request flow, write the actual prompt and its structured output format, and discuss retrieval-augmented generation, hallucination, prompt injection, and how to evaluate and monitor the feature.
ML System DesignOnsite - 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 - IntuitMediumSoftware EngineerDesign a "Future Best-Sellers" Prediction and Recommendation System
This question tests the ability to design an end-to-end machine learning system that predicts future best-sellers and serves real-time recommendations, balancing model accuracy, batch processing, and serving latency.
ML System DesignTechnical ScreenSystem DesignMLOpsRecommendations - KikoffMediumSoftware EngineerDesign a Credit Scoring Platform
This question assesses the ability to design an end-to-end machine learning system for real-time credit risk assessment, focusing on data pipelines, model inference, and regulatory compliance.
ML System DesignOnsiteScalabilityFeature StoreCompliance - LambdaHardSoftware EngineerDesign a cloud AI inference platform
This question evaluates competency in ML system design and operational engineering, covering model packaging and versioning, hardware selection (CPU vs GPU), autoscaling, request routing, multi-tenant isolation, SLO-driven observability, cost controls, and failure handling within a cloud inference platform.
ML System DesignHR Screen - Luma AIHardSoftware EngineerManage Prompt and Model Behavior in Production
This question tests the ability to design an end-to-end configuration management and observability system for LLM applications that ensures reproducibility, safety, and rapid recovery from performance drift.
ML System DesignTechnical ScreenLLMOpsSystem DesignObservability - MetaHardSoftware EngineerBuild a Mistral-powered RAG agent
This question evaluates a candidate's ability to design and implement a retrieval-augmented generation (RAG) system using a large language model API, including document ingestion, chunking, vector indexing and retrieval, streaming completions, service interfaces (CLI and HTTP), and resilience features like retries, persistence, and structured error handling. Commonly asked in the ML System Design domain to assess practical engineering and architectural reasoning, it tests API integration, production-readiness (configuration, error handling, persistence, and testing) and emphasizes practical application with conceptual understanding of retrieval trade-offs.
ML System DesignOnsite - MetaMediumSoftware EngineerDesign a Code Review Agent
This question evaluates a candidate's ability to design ML-powered developer tooling, covering competencies in LLM orchestration, deterministic tool integration, repository context management, code-analysis heuristics, safety and validation strategies, and inference cost estimation.
ML System DesignOnsite - MetaHardSoftware EngineerDesign an Automated Ticket Investigation Agent
This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.
ML System DesignTechnical Screen - MetaHardSoftware EngineerDesign place-of-interest ML system
Design place-of-interest ML system 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 DesignOnsite - MetaMediumSoftware EngineerExplain an AI System You Designed and the Problem It Solved
Tests your ability to articulate an end-to-end production AI/ML system you designed, detailing the business justification, technical architecture, evaluation strategy, failure modes, and your individual contributions.
ML System DesignTechnical Screenml-system-designai-architecturemodel-evaluation - MicrosoftMediumSoftware EngineerDesign Chatbot Personalization Memory
This problem evaluates the ability to design a scalable, low-latency memory system for an LLM-based chatbot that stores and retrieves user-specific facts to provide personalized responses.
ML System DesignOnsiteSystem DesignRAGLLM - MicrosoftMediumSoftware EngineerDesign a RAG Ranking Pipeline
This problem tests your ability to design a multi-stage retrieval and ranking architecture for a RAG system, focusing on balancing search accuracy, latency, and system scalability.
ML System DesignOnsiteRAGInformation RetrievalSystem Design - MicrosoftHardSoftware EngineerShard a Large Input Embedding Table Across Devices
Compare vocabulary and hidden-dimension embedding sharding through per-rank storage, token routing, hidden-state layout, skew, and downstream communication.
ML System DesignOnsite - MicrosoftHardSoftware EngineerShard an LM Head and Combine Distributed Logits
Shard an LM head by vocabulary or hidden width, distinguishing complete logits from partial dot products and exact distributed top-k from full-distribution sampling.
ML System DesignOnsite - Mistral AIHardSoftware EngineerBuild and design a Mistral RAG agent
This question evaluates a candidate's skills in building a retrieval-augmented generation (RAG) system, covering document ingestion, chunking, embedding generation, vector indexing, retrieval, LLM API-driven streaming completions, short-term conversation memory, and operational reliability features.
ML System DesignTechnical Screen - Mistral AIHardSoftware EngineerDesign a PDF-to-Markdown Inference API
This question evaluates system-design and ML inference orchestration skills, assessing competency in composing multi-stage pipelines that manage heterogeneous resource profiles (CPU, GPU, memory) and trade-offs between latency and throughput.
ML System DesignTechnical Screen - NVIDIAMediumSoftware EngineerDescribe model-to-GPU execution pipeline
Describe model-to-GPU execution pipeline 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 - NVIDIAMediumSoftware EngineerDesign and benchmark optimized inference pipelines
Design and benchmark optimized inference pipelines 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 - NVIDIAEasySoftware EngineerDesign real-time fraud detection under 50ms
This question evaluates expertise in designing low-latency, high-throughput ML-powered systems for online decisioning, covering competencies in real-time inference, feature pipelines, model serving, scaling, and operational reliability within the ML System Design domain.
ML System DesignTechnical Screen - NVIDIAMediumSoftware EngineerExplain ML compilation optimizations and hardware fit
This question evaluates a candidate's understanding of ML compiler optimizations and hardware-aware runtime strategies, assessing competencies in techniques such as kernel fusion, quantization, memory planning, scheduling/tiling, layout selection, sparsity, mixed precision, graph-level rewrites and runtime tactics.
ML System DesignTechnical Screen - NVIDIAHardSoftware EngineerExplain ML framework trends
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 Explain ML framework trends states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
ML System DesignTechnical Screen - NVIDIAMediumSoftware EngineerHow would you optimize large-scale training/inference?
This problem tests your understanding of performance bottlenecks in large-scale machine learning systems, requiring you to prioritize optimization strategies across memory, compute, and communication dimensions.
ML System DesignTechnical ScreenML SystemsGPU ArchitecturePerformance Engineering - NetappMediumSoftware EngineerDesign a Production Retrieval-Augmented Generation Pipeline
This problem tests the ability to design an end-to-end RAG system, focusing on document processing, embedding strategies, retrieval mechanisms, and ensuring accuracy through grounding.
ML System DesignOnsiteRAGLLMVector Database - NetflixHardSoftware EngineerBuild a Recommendation System and Conversational Interface from Scratch
Design an initial recommendation system, then extend it with a chatbot that can retrieve and explain recommendations while respecting the same eligibility and ranking rules. Connect data and model choices to serving architecture, latency and throughput, evaluation, monitoring, failure modes, and iteration.
ML System DesignOnsite - NikeMediumSoftware EngineerDesign a PDF Ingestion and Retrieval-Augmented Generation Pipeline
This problem tests the ability to design an end-to-end RAG pipeline capable of handling complex document parsing, chunking strategies for citations, and efficient synchronization of updates.
ML System DesignTechnical ScreenRAGPDF ParsingVector Databases - NuroHardSoftware EngineerDesign a Shared Distributed ML Training Platform
Design a multi-tenant distributed machine learning training platform that orchestrates GPU clusters, enforces team quotas, guarantees gang scheduling, and handles node-level checkpointing and recovery.
ML System DesignOnsiteml-system-designdistributed-systemscluster-management - OpenAIHardSoftware EngineerDesign a Retrieval-Augmented Generation (RAG) system
This question tests the ability to design an end-to-end Retrieval-Augmented Generation (RAG) system, focusing on balancing retrieval accuracy, latency, data freshness, and system reliability.
ML System DesignTechnical ScreenRAGLLMVector Databases
Intuit
Luma AI
Meta
Microsoft
Mistral AI
NVIDIA
Netflix
Nuro
OpenAI