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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 121 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 - 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 - 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 - 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 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 - 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 - 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 - 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 - 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 - 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
Adobe
Airbnb
Airtable
Amazon
Anthropic
Apple