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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
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- 11
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- 8
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Showing 5791–5820 of 9403 results
- 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 - NVIDIAHardData ScientistDesign and explain robust web APIs for ML inference
This question evaluates a candidate's proficiency in designing HTTP REST APIs for ML inference, including API endpoints, versioning, idempotency, request/response schemas, authentication/authorization, rate limiting, observability, and backward-compatibility policies, and it falls under the Coding & Algorithms domain for a data scientist role.
Coding & AlgorithmsHR 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 - NVIDIAHardSoftware EngineerDesign signals across power and clock domains
An NVIDIA software/hardware engineering onsite system-design question on architecting a control-signal interface that crosses both power and clock domains in a SoC. Covers level shifters and isolation cells with always-on vs. switchable placement, clock-domain-crossing schemes (two-flop synchronizers, pulse-to-toggle, request/ack handshakes, async FIFOs) and their STA/CDC constraints, the same-clock simplification, and the A→B→A feedthrough case with clamp choice, retention, wake-up sequencing, and UPF/CPF low-power signoff.
System DesignOnsite - NVIDIAMediumSoftware EngineerDesign the Control Plane for a Compute Cluster: Host Monitoring + Job Dispatch
Design a distributed control plane for a large compute cluster that ingests high-frequency host health and resource heartbeats while scheduling and dispatching jobs onto healthy hosts. The problem evaluates distributed state management, data modeling under write-heavy loads, failure detection, and high-availability coordinator design.
System DesignTechnical ScreenDistributed SystemsCluster ManagementHigh Throughput - NVIDIAHardSoftware EngineerDetermine Whether One Point Can Reach Another
Determine whether a starting point (sx, sy) can reach a target (tx, ty) on a 2D integer grid via addition moves, optimized for coordinates up to 10^18.
Coding & AlgorithmsTechnical ScreenMathNumber TheoryGreedy - NVIDIAHardData ScientistDiagnose overfitting, DenseNet, preprocessing, CV
This question evaluates a candidate's understanding of overfitting and mitigation techniques, comparative architecture knowledge between DenseNet and ResNet, leakage-free preprocessing and harmonization for medical imaging, practical CNN hyperparameter tuning, and cross-validation strategies.
Machine LearningHR Screen - NVIDIAHardMachine Learning EngineerDiscuss Transformer LLM Design
Discuss Transformer LLM Design 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 EngineerDiscuss activities, role, and project deep dive
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Discuss activities, role, and project deep dive states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnsite - NVIDIAMediumSoftware EngineerExplain Amdahl’s law and GPU matmul optimization
This question tests the ability to apply Amdahl's Law to real-world hardware acceleration and understand the architectural constraints involved in GPU matrix multiplication.
Software Engineering FundamentalsTechnical ScreenAmdahl's LawGPU ArchitectureC++ Performance - 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 - NVIDIAMediumData ScientistExplain NVIDIA fit and role value
This question evaluates a candidate's cultural and role fit, ability to communicate measurable impact, skill in mapping past data-science projects to posted responsibilities, awareness of recent organizational initiatives, and adaptability to team culture.
Behavioral & LeadershipHR Screen - NVIDIAMediumSoftware EngineerExplain Transformers and QKV matrices
This question evaluates conceptual and mathematical understanding of the Transformer self-attention mechanism, including how query, key, and value matrices are derived and used. It assesses depth of knowledge in modern deep learning architectures, commonly asked in machine learning and software engineering interviews to test reasoning about sequence modeling and attention-based design.
Machine LearningTechnical Screen - NVIDIAMediumSoftware EngineerExplain a graphics testing project in depth
This interview question evaluates behavioral evidence, ownership, communication, trade-offs, and measurable outcomes in a realistic interview setting. A strong answer for Explain a graphics testing project in depth states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Behavioral & LeadershipOnline Assessment - NVIDIAMediumSoftware EngineerExplain a shader compiler pipeline
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Explain a shader compiler pipeline states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnline Assessment - NVIDIAMediumMachine Learning EngineerExplain bias-variance, calibration, and model drift
This question tests your conceptual understanding of model performance degradation over time, the significance of calibrated outputs for decision-making, and the fundamental trade-off between bias and variance.
Machine LearningTechnical ScreenModel EvaluationProduction MLBias-Variance
NVIDIA