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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.
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- 546
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Showing 91–120 of 121 results
- OpenAIHardSoftware EngineerDesign a Text-to-Video Generation Platform (Sora-style)
This problem evaluates your ability to design a distributed ML infrastructure that handles high-latency, resource-intensive inference jobs while ensuring scalability, reliability, and content safety.
ML System DesignOnsiteDistributed SystemsML InfrastructureTask Queues - OpenAIMediumSoftware EngineerDesign a Text-to-Video Generation Service
This question tests the ability to design a scalable, reliable asynchronous inference pipeline for resource-intensive generative models, focusing on task orchestration, GPU scheduling, and latency management.
ML System DesignTechnical ScreenML-SystemsDistributed-SystemsGPU-Orchestration - OpenAIHardSoftware EngineerDesign a Text-to-Video Generation System
This question evaluates a candidate's competency in designing scalable ML inference platforms, including GPU resource management and scheduling, asynchronous job orchestration and state-machine-driven job lifecycles, failure classification and recovery, artifact storage, rate limiting, and monitoring for quality and latency.
ML System DesignOnsite - OpenAIMediumSoftware EngineerDesign a Video Generation Service with GPU Fleet Management
Tests the ability to design an end-to-end distributed ML inference system for compute-intensive video generation, focusing on GPU scheduling, job lifecycle management, fault tolerance, and cost efficiency.
ML System DesignTechnical ScreenGPU SchedulingDistributed SystemsFault Tolerance - OpenAIMediumSoftware EngineerDesign a chatbot (system design)
This question evaluates system design skills for ML-powered chatbots with a front-end focus, testing understanding of client-side privacy and storage lifetimes, streamed response delivery, stateless relay architectures, authentication boundaries, rate limiting, and failure-mode handling.
ML System DesignTechnical Screen - OpenAIHardSoftware EngineerDesign a response-ranking ML system
Design a response-ranking 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 DesignTechnical Screen - OpenAIHardSoftware EngineerDesign an enterprise RAG assistant for internal docs
This question evaluates expertise in designing and training Retrieval-Augmented Generation (RAG) systems, including retriever, evaluator (reranker/verifier/filter), and generator components, with emphasis on model architecture choices, training objectives, data preparation under privacy and document-permission constraints, and evaluation strategies for grounded answers with citations. It is commonly asked to probe advanced ML system design and operationalization skills for mitigating hallucination, stale or conflicting sources, and long-document retrieval; the category is ML System Design and the level is practical application-focused with detailed modeling and training considerations.
ML System DesignTechnical Screen - OracleMediumSoftware EngineerDesign an E-Commerce Recommendation System from Browsing History
This problem tests the ability to architect a scalable, low-latency recommendation engine that translates real-time user activity into personalized product suggestions.
ML System DesignOnsiteSystem DesignRecommendation SystemsScalability - OracleHardSoftware EngineerDesign scalable, highly available GenAI serving
This question evaluates understanding of scalable, highly available generative AI inference platforms and associated competencies in distributed systems, ML model serving, autoscaling and GPU scheduling, global request routing, model/version management, stateful dependency handling, observability, and rate limiting.
ML System DesignOnsite - OracleHardSoftware EngineerEvaluate Subjective and Nondeterministic Agent Outputs
Create an evaluation program for subjective, nondeterministic AI-agent outputs when direct user feedback is sparse. Cover stratified tasks, versioned rubrics, repeated sampling, calibrated human review, judge agreement and bias, uncertainty-aware comparisons, severe-failure gates, online signals, and rollback criteria.
ML System DesignTechnical Screen - Palo Alto NetworksHardSoftware EngineerDesign an ML inference orchestration platform
This question evaluates system-design and ML infrastructure competencies, focusing on orchestration of multi-model inference workflows, distributed data flow and storage, versioning and model management, scaling and deployment, failure handling, observability, and multi-tenant security and quota management.
ML System DesignOnsite - PerplexityMediumSoftware EngineerDesign personalized discovery recommendations
This problem evaluates your ability to design a scalable, real-time recommendation system that balances personalization with content discovery in an information-seeking environment.
ML System DesignOnsiteRecommender SystemsML InfrastructureRanking - PerplexityMediumSoftware EngineerDesign trending queries ranking system
This question tests the ability to design a high-throughput, real-time pipeline that identifies and ranks trending search queries, requiring trade-offs between freshness, accuracy, and system latency.
ML System DesignOnsiteSystem DesignReal-time StreamingRanking - PerplexityHardSoftware EngineerImplement resilient LLM provider pool
This English-language summary evaluates a candidate's ability to design a resilient, multi-provider LLM client library encompassing provider management, governance (rate limiting and quotas), resilience mechanisms (health checks, timeouts, circuit breakers), routing (cost- and latency-aware load balancing, retries and fallbacks), observability, security, and concurrency. Commonly asked in the ML System Design domain to assess architectural decision-making and trade-off reasoning for availability, cost, latency, and operational robustness, the question targets practical application and architectural-level thinking rather than purely conceptual or low-level implementation details.
ML System DesignTechnical Screen - PinterestHardSoftware EngineerDesign Pin recommendation system
This question evaluates ability to design a large-scale personalized home feed recommendation system, testing skills in ML system design, recommender algorithms, retrieval and ranking architectures, feature engineering, data pipelines, low-latency serving, evaluation metrics, safety, and privacy.
ML System DesignOnsite - RedditHardSoftware EngineerDesign a feature store with CI/CD and reliability
This question evaluates a candidate's competencies in designing scalable, low-latency feature stores and end-to-end ML infrastructure, covering feature ingestion, point-in-time correctness, serving APIs, CI/CD for feature pipelines, metadata/lineage, and reliability engineering.
ML System DesignTechnical Screen - RedditMediumSoftware EngineerDesign comment-likelihood prediction platform
This question evaluates a candidate's competency in ML system design and infrastructure, covering feature pipelines, feature store strategy, training/serving consistency, online inference at high QPS, and production monitoring.
ML System DesignOnsite - RobloxHardSoftware EngineerDesign a Static Audio Detection System
This question evaluates system design and ML integration skills for building a scalable, reliable offline audio detection pipeline, including ingestion, preprocessing, STT and spectral feature extraction, rule-based post-processing, artifact persistence, and human-in-the-loop review within the ML system design domain.
ML System DesignOnsite - RobloxEasySoftware EngineerDesign a game recommendation modeling approach
This problem tests the ability to design an end-to-end recommendation engine for a massive-scale gaming platform, focusing on user-item interaction modeling and handling cold-start scenarios.
ML System DesignTechnical ScreenRecommender SystemsML DesignRetrieval - RobloxHardSoftware EngineerOutline the ML inference and labeling pipeline
Outline the ML inference and labeling 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 DesignOnsite - SalesforceMediumSoftware EngineerDesign a recommendation system
This problem tests the ability to design an end-to-end recommendation engine, focusing on the pipeline from data ingestion to real-time inference and model evaluation.
ML System DesignTechnical ScreenRecommendation SystemsScalabilityLatency - SalesforceHardSoftware EngineerDesign an end-to-end recommendation system
Design an end-to-end recommendation 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 DesignTechnical Screen - Scale AIEasySoftware EngineerDesign an LLM API pipeline
This question evaluates competency in integrating hosted LLMs into user-facing applications, including API request design, prompt engineering for structured output, output validation, latency and failure handling, and production readiness.
ML System DesignOnsite - Scale AIMediumSoftware EngineerDesign pipeline using classification and embedding services
This question evaluates a candidate's proficiency in ML system design, covering API design, service orchestration, data storage modeling, scalability strategies, and reliability/observability when integrating black-box classification and embedding services.
ML System DesignOnsite - TikTokMediumSoftware EngineerDesign system to detect privacy-leak records
This question evaluates a candidate's ability to design scalable ML-driven systems for detecting and classifying privacy-sensitive and PII-containing records across structured and unstructured data, testing competencies in data engineering, machine learning (including deep learning and LLM/RAG integrations), system architecture, and privacy/security considerations. It is commonly asked to probe reasoning about functional and non-functional requirements, trade-offs in detection and classification approaches, evaluation metrics like precision and recall, feedback loops and operational scaling, and it falls under ML system design with a practical, application-level focus rather than purely conceptual abstraction.
ML System DesignTechnical Screen - Two SigmaMediumSoftware EngineerDesign GenAI Fine-Tuning and Agent Tradeoffs
Design an enterprise GenAI research agent platform, evaluating trade-offs between fine-tuning methods (Full, LoRA, QLoRA) versus RAG, alongside agent orchestration, structured outputs, and safety controls.
ML System DesignTechnical ScreenLLM Fine-TuningAI AgentsModel Serving - UnityMediumSoftware EngineerDesign a Feature Store for Offline Training and Low-Latency Online Serving
Design a feature store that ingests high-volume event data, produces point-in-time correct training sets, and serves the latest features at low latency in production. It tests streaming and batch feature computation, leakage-free historical joins, online store design, and keeping training and serving consistent.
ML System DesignOnsite - VantaMediumSoftware EngineerReview and Debug an AI Chatbot
Design an end-to-end debugging, observability, and evaluation architecture for a multi-tenant enterprise AI compliance chatbot experiencing hallucinations, latency spikes, and policy violations.
ML System DesignOnsiteLLM ObservabilityRAGData Privacy - WaymoMediumSoftware EngineerDesign a Hybrid Evaluation Platform
This problem tests your ability to design a multi-modal evaluation infrastructure that balances automated LLM-based grading with human-in-the-loop (HITL) verification to ensure high-fidelity ML model performance metrics.
ML System DesignOnsiteMLOpsData InfrastructureHuman-in-the-loop - WorkdayMediumSoftware EngineerDesign AI-Powered Document Search
This question assesses the ability to design an end-to-end, scalable pipeline for ingesting, processing, and indexing heterogeneous documents (PDFs, images, text files) to support AI-driven semantic search.
ML System DesignOnsiteSystem DesignML EngineeringPipelines
OpenAI
Oracle
Palo Alto Networks
Perplexity
Pinterest
Reddit
Roblox
Salesforce
Scale AI
TikTok
Two Sigma
Vanta
Waymo