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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
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- 546
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- 11
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Showing 3631–3660 of 5874 results
- NVIDIAMediumSoftware EngineerClarify and Design a Streaming Top-K Tracker
This question tests the ability to design an efficient data structure for maintaining the top-K elements in a high-throughput stream with time-windowing requirements.
Software Engineering FundamentalsTechnical ScreenData StructuresAlgorithmsStreaming - NVIDIAMediumSoftware EngineerCompare Java and Python: Execution, Typing, Concurrency and When to Use Each
An engineering director asks how Java and Python differ and where each language is stronger or weaker. It tests understanding of execution models, static versus dynamic typing, concurrency and the global interpreter lock, ecosystems, and the judgment to recommend a language for a concrete project.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerCompare deep learning framework trends
Compare deep learning framework trends evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - NVIDIAEasySoftware EngineerCompute the Final Robot Score
Tests proficiency in using priority queues (heaps) to manage dynamic multisets under iterative reductions. Evaluates the ability to simulate priority-based selection and reason about time and space complexity.
Coding & AlgorithmsTechnical ScreenHeapPriority QueueSimulation - NVIDIAMediumSoftware EngineerConstruct tree from inorder & postorder
This question evaluates understanding of binary tree reconstruction and traversal order relationships, specifically the ability to derive tree structure from inorder and postorder arrays and implement the corresponding algorithm.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerCount Distinct Strings With a Hand-Built Hash Table, Then Parallelize It for GPUs
A coding and concurrency question: count the distinct strings in an array with a hash table built from scratch, then keep the count correct when many threads insert at once. It tests hash table design, locks versus atomic compare-and-swap, and how work division and memory layout affect GPU and CPU performance for a million keys.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerCount Server Pairs Relayed Through Each Node of a Weighted Tree
Given a weighted tree of servers and a signal speed, count for every server how many pairs of other servers it can relay: both path lengths must be multiples of the speed and the two paths must share no cable. Tests tree traversal, path-length reasoning and careful combinatorial counting.
Coding & AlgorithmsOther - NVIDIAHardSoftware EngineerCount Substrings with No Repeated Characters
Tests sliding window / two-pointer techniques to count all valid substrings without duplicate characters in linear time.
Coding & AlgorithmsTechnical Screensliding-windowtwo-pointersstrings - NVIDIAMediumSoftware EngineerDefine a Git workflow for CI
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Define a Git workflow for CI states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsOnline Assessment - NVIDIAMediumSoftware EngineerDefine developer-centric usability metrics
This question evaluates proficiency in analytics and experimentation and the ability to define and operationalize developer-centric usability metrics for both consumer-facing applications and developer tools.
Analytics & ExperimentationTechnical Screen - NVIDIAMediumSoftware EngineerDemonstrate software engineering fundamentals
This interview question evaluates engineering fundamentals, trade-offs, examples, pitfalls, and operational implications in a realistic interview setting. A strong answer for Demonstrate software engineering fundamentals states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerDerive MLP shapes and explain PyTorch broadcasting
This question tests the ability to calculate matrix transformation dimensions in a Multilayer Perceptron (MLP) and explains the rules governing PyTorch broadcasting when performing operations between tensors of different shapes.
Machine LearningTechnical ScreenPyTorchLinear AlgebraTensors - 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 - NVIDIAHardSoftware EngineerDesign a Dockerized GPU test pipeline
This interview question evaluates requirements, scale assumptions, API/data design, architecture, trade-offs, failure modes, and rollout in a realistic interview setting. A strong answer for Design a Dockerized GPU test pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnline Assessment - NVIDIAMediumSoftware EngineerDesign a Key-Value Store, From the Single-Node Storage Engine to Replication
A system design interview asks you to design a key-value store supporting put, get and delete. It tests how you scope consistency and durability, build a single-node storage engine, partition keys across machines, replicate data, and handle node failures, rebalancing and hot keys.
System DesignOnsite - NVIDIAHardSoftware EngineerDesign a Multi-Producer Multi-Consumer Circular Buffer
Design a thread-safe, bounded multi-producer multi-consumer (MPMC) circular buffer supporting blocking operations, timeouts, and graceful shutdown (close-and-drain).
Software Engineering FundamentalsTechnical ScreenConcurrencyData StructuresMultithreading - NVIDIAMediumSoftware EngineerDesign a Task Scheduler That Runs Each Task Reliably When It Is Due
A system design round asks you to design a task scheduler that accepts tasks from clients and makes sure each one runs when it is due. It tests scoping, data modeling, finding due tasks efficiently, preventing double execution, handling worker and scheduler failures, and keeping scheduling lag low during bursts.
System DesignOnsite - NVIDIAHardSoftware EngineerDesign a URL shortening service
This problem tests your ability to design a high-scale, low-latency URL shortening service that requires distributed ID generation, efficient storage strategies, and high availability.
System DesignOnsiteCachingScalabilityDatabase - NVIDIAMediumSoftware EngineerDesign a Version-Aware Package Download Service That Fetches Missing Dependencies
Design a download service for versioned software packages that, when a user requests a version such as Python 2.7, also fetches every dependency missing from their machine. It tests dependency resolution with version ranges, conflicts with already-installed packages, reliable downloads and installs, and serving large artifacts at scale.
System DesignOnsite - NVIDIAMediumSoftware EngineerDesign a bidirectional data sync dashboard
This question assesses the ability to design a distributed system that synchronizes data between a central dashboard and multiple external third-party services in real-time.
System DesignOnsiteDistributed SystemsAPIsData Consistency - NVIDIAHardSoftware EngineerDesign a distributed multi-user counter
This question evaluates expertise in distributed systems architecture, concurrency control, strong consistency models, idempotency under retries, fault tolerance, leader election, and operational monitoring for high-throughput services.
System DesignTechnical Screen - NVIDIAMediumSoftware EngineerDesign a top-build target tracker
This problem tests object-oriented design and data structure proficiency by requiring the implementation of a tracker that maintains the top-K most frequent entities within a sliding time window of events.
Software Engineering FundamentalsTechnical ScreenDesignData StructuresSliding Window - NVIDIAMediumSoftware EngineerDesign an Autonomous-Vehicle Camera Pipeline and Its Perception System
Design an autonomous-vehicle camera pipeline, from synchronized capture and image signal processing to the perception system that turns multi-camera frames into tracked objects for planning. The question tests time synchronization, zero-copy buffering and back-pressure in real-time C++, latency budgeting, multi-camera fusion and tracking, and fault detection with degraded modes.
System DesignOnsite - NVIDIAMediumSoftware EngineerDesign an Autonomous-Vehicle Perception Pipeline
Tests system design principles for real-time, safety-critical autonomous vehicle perception, focusing on multi-sensor hardware synchronization, zero-copy memory pipelines, bounded-latency neural inference, and fail-operational degradation.
System DesignTechnical ScreenAutonomous VehiclesComputer VisionReal-Time Systems - NVIDIAMediumSoftware EngineerDesign an IR for test workflows
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Design an IR for test workflows states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Software Engineering FundamentalsOnline Assessment - NVIDIAHardSoftware EngineerDesign an artifact store on K8s and Cassandra
This question evaluates distributed-systems and system-design competencies, including concurrency control for unique-name creation, idempotency and retry semantics, deletion modeling (soft deletes, tombstones, TTL), and read-path consistency, caching and performance when running a Java API on Kubernetes with Cassandra storage.
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 - NVIDIAMediumSoftware EngineerDesign and implement an LRU cache
This problem tests your ability to design a cache that supports O(1) time complexity for both insertions and lookups by combining a hash map with a doubly linked list.
Coding & AlgorithmsTechnical ScreenHash MapLinked ListCaching - NVIDIAMediumSoftware EngineerDesign first-time Kubernetes deployment in new cloud
This problem tests your ability to design a foundational Kubernetes infrastructure architecture in a new cloud environment, focusing on secure bootstrapping and operational readiness.
System DesignTechnical ScreenKubernetesCloud ArchitectureInfrastructure as Code - 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
NVIDIA