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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 1651–1680 of 2810 results
- 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 - 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 - 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 - 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 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 - 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 - 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 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 - 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 - 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 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 - 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 - NVIDIAEasySoftware EngineerReturn all file paths via DFS
This question evaluates understanding of tree traversal and depth-first search along with competence in handling hierarchical data structures and constructing full file paths.
Coding & AlgorithmsTechnical Screen - NVIDIAMediumSoftware EngineerSolve a Difficult Problem and Navigate Disagreement
Assesses technical problem-solving rigor, hypothesis testing, and the ability to resolve technical disagreements using data, empathy, and shared goals.
Behavioral & LeadershipTechnical ScreenBehavioralConflict ResolutionProblem Solving - NVIDIAMediumSoftware EngineerSolve anagram grouping and in-place allocator
This problem tests string hashing/sorting techniques for grouping anagrams and the ability to manage fixed-size memory blocks in-place without auxiliary data structures.
Coding & AlgorithmsTechnical ScreenHash MapsString ManipulationTwo Pointers - NVIDIAMediumSoftware EngineerSolve unique triplets summing to target
Solve unique triplets summing to target evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Coding & AlgorithmsTechnical Screen
NVIDIA