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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 62 results
- AMDMediumSoftware EngineerCompare Single-Agent and Multi-Agent Designs
Compare single-agent and multi-agent systems through task independence, specialization, review isolation, coordination cost, shared-state control, and evidence-based integration.
ML System DesignOnsite - AMDMediumSoftware EngineerEvaluate Agents That Generate or Optimize Kernels and Compiler Code
Evaluate kernel and compiler agents with bounded transformation spaces, isolated execution, differential correctness checks, numerical tolerances, representative benchmarks, and reviewable evidence.
ML System DesignOnsite - AMDMediumSoftware EngineerTrace an LLM Request Through a Paged-KV Inference Engine
Trace LLM inference from request admission through prefill and decoding, explaining paged KV block tables, continuous batching, cache lifetime, and latency tradeoffs.
ML System DesignOnsite - AirbnbHardSoftware EngineerDesign a dynamic rental pricing system
This question evaluates skills in production machine learning system design for pricing, including problem formulation, data and feature engineering, modeling and uncertainty management, online inference and scalability, experimentation and guardrails, and considerations like fairness and cold-start handling.
ML System DesignOnsite - AirtableMediumSoftware EngineerDesign NL-to-Formula assistant for Airtable
Airtable software-engineer onsite ML system design question: design an assistant that turns natural-language requests into the product's formula language, grounded in the live table schema, and optionally applies them to the base via the Airtable API. It tests LLM orchestration with structured outputs / a typed IR, schema grounding and type checking, ambiguity handling, static + dynamic formula validation, prompt-injection safety, confirmation-gated writes, latency trade-offs, and offline/online evaluation.
ML System DesignOnsite - AmazonMediumSoftware EngineerDesign a Platform for Training and Serving ML Models
This problem tests the ability to design an end-to-end MLOps platform capable of handling the full lifecycle of machine learning models, from experimentation to production deployment.
ML System DesignOnsiteMLOpsSystem DesignScalability - AnthropicMediumSoftware EngineerDesign GPU inference request batching
This question evaluates a candidate's competency in ML System Design, focusing on GPU-based inference batching, scheduling, autoscaling, and the operational concerns of routing, compatibility, and fault-tolerance.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign Model Weight Distribution
This question evaluates a candidate's competency in large-scale ML system design and distributed systems, focusing on weight file distribution, versioning and rollout strategies, consistency models, integrity verification, access control, rollback mechanisms, and operational scalability.
ML System DesignOnsite - AnthropicMediumSoftware EngineerDesign Safe Distribution and Activation of Model Weights
This problem tests the ability to architect a robust, secure, and atomic deployment system for large binary artifacts (model weights) across a distributed inference cluster.
ML System DesignOnsiteDistributed SystemsMLOpsSystem Design - AnthropicHardSoftware EngineerDesign a GPU inference API
This Anthropic ML system design onsite asks you to design a multi-tenant, GPU-backed inference API that serves many models and versions -- including large autoregressive LLMs -- under explicit latency SLOs. It spans API and schema design, streaming and async jobs, continuous batching with prefill/decode separation, KV-cache and GPU memory management, multi-model hosting on shared GPUs, heterogeneous autoscaling and placement, multi-region routing, canary rollout, and reliability and cost controls. The signature probe is the diagnostic scenario where CPU utilization is low but the GPUs are saturated.
ML System DesignOnsite - AnthropicHardSoftware EngineerDesign a batch inference API
This question evaluates a candidate's ability to design asynchronous batch inference APIs and systems, including API schema and job lifecycle design, idempotency semantics, queueing and worker scaling, batching and accelerator utilization, rate limiting, observability, and error handling.
ML System DesignOnsite - AnthropicHardSoftware EngineerDesign a high-concurrency LLM inference service
This problem evaluates your ability to design a scalable LLM inference serving platform, focusing on throughput optimization, memory constraints, and scheduling strategies for long-running inference requests.
ML System DesignOnsiteLLMInferenceGPU - AnthropicHardSoftware EngineerDesign a low-latency ML inference API
This question evaluates competency in ML system design for real-time, low-latency inference APIs, including multitenancy, SLO/SLI definition, feature retrieval, model serving and rollout strategies, observability, cost control, and security/compliance within the ML System Design category.
ML System DesignOnsite - AnthropicHardSoftware EngineerDesign a prompt processing backend
Design a prompt processing backend 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 - AnthropicHardSoftware EngineerEstimate FFN Compute, Memory, and Sharding Communication
Estimate standard FFN FLOPs, parameter and activation bytes, and sharding communication, then compare compute, HBM, and network limits with explicit layout assumptions.
ML System DesignOnsite - AnthropicHardSoftware EngineerEstimate VRAM and compare model parallelism
This question tests the ability to calculate memory overhead for transformer model parameters and activation buffers, while evaluating the architectural trade-offs between tensor and pipeline parallelism for large-scale training.
ML System DesignOnsiteDistributed SystemsGPU ArchitectureTransformer Training - AnthropicMediumSoftware EngineerPerformance-Model a Sharded Matrix Multiply: FLOPs, Memory, Network and Roofline
Model the performance of a matrix multiplication by hand: count floating-point operations, memory bytes and network bytes, apply the roofline model to decide what bounds it, and compare sharding along each dimension across several accelerators. It tests arithmetic intensity, collective communication costs and quantitative reasoning.
ML System DesignOnsite - AnthropicHardSoftware EngineerReview an inference API design for scale
This question tests a candidate's ability to critically evaluate ML system designs for production-scale inference APIs, covering multi-tenancy, GPU resource constraints, and streaming token delivery. It assesses architectural reasoning across reliability, latency trade-offs, and capacity planning in the ML Systems domain.
ML System DesignOnsite - 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 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 - 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 - 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 - 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 - 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 - 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 - 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
Airbnb
Airtable
Amazon
Anthropic
Cresta
Databricks
Decagon
DoorDash
Figma
Grammarly
Harvey