practice a little every day, paws crossed ♡
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 5821–5850 of 9403 results
- NVIDIAMediumSoftware EngineerExplain container image flow in CI/CD
This question evaluates understanding of the container image lifecycle within CI/CD pipelines, covering build mechanics (layers, cache, build context), tagging and registry interactions, image distribution and pulling behavior (authentication, caching, digests), security controls (vulnerability scanning, SBOM generation, signing), and common failure modes. It is commonly asked to gauge operational and security competence in the Software Engineering Fundamentals/DevOps domain and measures both conceptual understanding and practical application of pipeline design and runtime image management.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerExplain linear algebra for graphics transforms
This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Explain linear algebra for graphics transforms states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Statistics & MathOnline Assessment - NVIDIAHardSoftware EngineerExplain optimization and tensor vs pipeline parallelism
This question evaluates knowledge of deep learning optimization techniques (quantization, pruning, knowledge distillation, kernel/operator fusion, memory and throughput optimizations) and model parallelism strategies (tensor versus pipeline), measuring competency in performance engineering, scalability, communication patterns and system-level trade-offs for training and inference. It is commonly asked to assess an engineer's ability to reason about practical and conceptual trade-offs, collectives, bottlenecks and mitigations in distributed and resource-constrained environments, and it falls under the Machine Learning/Deep Learning domain requiring both conceptual understanding and practical application.
Machine LearningTechnical Screen - NVIDIAHardSoftware EngineerExplain shader compilers and graphics APIs
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 Explain shader compilers and graphics APIs states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignOnsite - NVIDIAMediumSoftware EngineerExplain virtual machines and concurrency basics
This question evaluates understanding of virtualization (virtual machine architecture, hypervisor types, and how CPU, memory, storage and networking are virtualized along with their performance and security tradeoffs) and concurrency fundamentals (distinction between concurrency and parallelism, common primitives like threads, locks and atomics, as well as race conditions, deadlocks and debugging production concurrency issues). It is commonly asked in Software Engineering Fundamentals interviews to assess system-level reasoning about trade-offs, isolation and safe concurrent design, and it tests both conceptual understanding and practical application at a senior-engineer depth.
Software Engineering FundamentalsTechnical Screen - NVIDIAHardSoftware EngineerFetch Paginated Medical Records and Filter by Field Rules and Historical Average Weight
A practical coding exercise: page through an HTTP endpoint that returns medical records 10 at a time, then return the records that pass a list of field comparisons and a weight condition based on the patient's historical average weight. It tests pagination, robust parsing, per-patient aggregation and working quickly from a long specification.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumSoftware EngineerFind Top K Frequent Elements
This question evaluates a candidate's proficiency with frequency counting, selection techniques, use of appropriate data structures, and algorithmic time and space complexity analysis.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerFind all unique triplets summing to zero
This question evaluates array-based algorithm design, handling of duplicate results, and analysis of time and space complexity when producing unique triplets that sum to zero.
Coding & AlgorithmsOnsite - NVIDIAMediumSoftware EngineerFind minimum time to cross bridge with flashlight
This problem tests your ability to model a greedy strategy and handle state transitions under constraints to minimize the total time taken for a group to cross a bridge.
Coding & AlgorithmsTechnical ScreenGreedySortingOptimization - NVIDIAEasySoftware EngineerFind the nth prime number
This question assesses algorithmic problem-solving and number-theoretic computation skills, requiring efficient prime generation and complexity analysis in the Coding & Algorithms domain.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerHash-Partition an Array on the GPU in C++ and Return Partition Offsets
A GPU programming question that asks you to hash-partition an array of keys into P partitions in CUDA C++, making each partition contiguous and returning each partition's starting offset. Follow-up questions test GPU optimization: atomic contention, memory coalescing, shared memory use and the number of passes over global memory.
Software Engineering FundamentalsTechnical Screen - NVIDIAMediumApplied ScientistHow AI Agents Fit Into Your Research Workflow: Ideas, Experiments, and Paper Writing
A research internship interview question about how AI agents change a researcher's daily workflow across generating ideas, running experiments, and writing papers. It tests concrete delegation choices, how agent output is verified, awareness of failure modes such as fabricated references, and judgment about confidentiality and reproducibility.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerHow AI Coding Tools Change Software Development and When to Trust Their Output
An open-ended question from a director round asks how AI is changing software development and whether engineers should trust AI-generated code. It tests a balanced position on benefits and failure modes, how generated code should be verified, team practices for review and security, and how to measure real impact.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerHow would you optimize large-scale training/inference?
This problem tests your understanding of performance bottlenecks in large-scale machine learning systems, requiring you to prioritize optimization strategies across memory, compute, and communication dimensions.
ML System DesignTechnical ScreenML SystemsGPU ArchitecturePerformance Engineering - NVIDIAMediumSoftware EngineerImplement 2D convolution using NumPy slicing
This question evaluates a candidate's proficiency with array manipulation and vectorized numerical computation using NumPy, focusing on 2D convolution concepts such as slicing, broadcasting, and filter application.
Data Manipulation (SQL/Python)Technical Screen - NVIDIAHardData ScientistImplement CUDA-tiled matrix multiplication and explain architecture
This question evaluates a candidate's competency in CUDA GPU programming, parallel algorithms, and performance engineering for FP32 matrix multiplication, covering tiling strategies, memory hierarchy (global/shared/register), synchronization, numerical precision, and occupancy/resource analysis.
Coding & AlgorithmsHR Screen - NVIDIAMediumSoftware EngineerImplement a Python test harness
This interview question evaluates SQL or pandas logic, joins, grouping, window functions, null handling, edge cases, and validation in a realistic interview setting. A strong answer for Implement a Python test harness states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Data Manipulation (SQL/Python)Online Assessment - NVIDIAMediumSoftware EngineerImplement a Thread-Safe Reference-Counted Smart Pointer in C++
Implement a reference-counted smart pointer class template in C++ from scratch, with copy and move construction and assignment, reset, dereferencing and a use count. It tests ownership rules, self-assignment and exception safety, atomic reference counting with correct memory ordering, and extensions such as weak pointers, base-class conversion and reference cycles.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerImplement and Explain a C++ Smart Pointer
Tests understanding of C++ memory management, RAII, explicit ownership semantics, and the Rule of Five by implementing a reference-counted shared smart pointer.
Software Engineering FundamentalsTechnical ScreenC++Memory ManagementRAII - NVIDIAMediumSoftware EngineerImplement core graph algorithms for graphics
This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Implement core graph algorithms for graphics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsOnline Assessment - NVIDIAHardSoftware EngineerImplement short algorithms on logs, grids, and strings
This problem tests your ability to handle multi-stage data processing, specifically focusing on greedy interval selection in logs, stack-based parsing of nested strings, and constrained combinatorial optimization on a grid.
Coding & AlgorithmsOnsiteDynamic ProgrammingStackBacktracking - NVIDIAMediumSoftware EngineerIntroduce yourself for a senior role
This question evaluates leadership, communication, ownership, and impact-articulation skills for a senior software engineer, focusing on the ability to concisely present scope, technical and business outcomes, and future alignment.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerMarket a new AI technique
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 Market a new AI technique states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
System DesignTechnical Screen - NVIDIAMediumData EngineerMerge Overlapping Time Intervals
Practice a NVIDIA coding interview problem focused on merge overlapping time intervals. The prompt emphasizes edge cases, clean implementation, and verifiable test behavior without revealing the solution.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerNormalize an Absolute Unix-Style File Path to Canonical Form
Convert an absolute Unix-style file path into canonical form by collapsing repeated slashes, dropping current-directory markers, applying parent-directory moves that stop at the root, and treating other dot-only names as ordinary directories. Tests careful string parsing and edge-case handling of path components.
Coding & AlgorithmsOther - NVIDIAHardData ScientistOptimize CUDA GEMM with tiling and coalescing
This question evaluates proficiency in GPU programming and performance optimization, assessing understanding of the CUDA execution model, memory hierarchy and bank conflicts, occupancy limits, and practical kernel design concerns for single-precision GEMM including tiling, coalescing, vectorized memory accesses and benchmarking.
Coding & AlgorithmsHR Screen - NVIDIAMediumSoftware EngineerOptimize a small-string C++ class
This question evaluates low-level C/C++ systems programming skills, including memory management, data layout, small-string optimization, efficient copying semantics, alignment/padding, and cache-aware performance reasoning.
Software Engineering FundamentalsOnsite - NVIDIAMediumSoftware EngineerPlan discovery and adoption strategy
This question evaluates a candidate's skills in product discovery, stakeholder alignment, technical communication, and developer adoption strategy for integrating a new ML technique, including defining use cases, constraints, baselines, and success criteria.
Behavioral & LeadershipTechnical Screen - NVIDIAMediumSoftware EngineerReason About Endianness, Ring Buffers, and Stack Behavior in Firmware
Tests firmware-level engineering concepts across low-level binary data parsing, lock-free ring buffer design for ISR-to-thread communication, and static/runtime stack depth analysis under nested interrupts.
Software Engineering FundamentalsTechnical ScreenFirmwareEmbedded SystemsConcurrency - NVIDIAMediumSoftware EngineerReconstruct tree from inorder and postorder
This question evaluates understanding of binary tree traversal relationships, recursive construction techniques, and algorithmic time and space complexity, reflecting core data structures and recursion competencies.
Coding & AlgorithmsTechnical Screen
NVIDIA