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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 31–53 of 53 results
- Mistral AIMediumSoftware EngineerExplain the Forward and Reverse Processes of Diffusion Models
Explain Gaussian diffusion corruption, timestep-conditioned denoising, noise-prediction training, iterative reverse sampling, latent representations, and guidance tradeoffs.
Machine LearningTechnical Screen - 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 - 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 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 - 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 - NewsBreakMediumSoftware EngineerFind Optimal Piecewise Constant Regression Parameters
This problem tests the ability to derive optimal constants for a piecewise regression model with a single split point and to implement an efficient search algorithm to minimize the sum of squared errors.
Machine LearningTechnical ScreenRegressionOptimizationPrefix Sums - NikeMediumSoftware EngineerWrite Pseudocode for Embedding Creation and Vector Search
This problem tests the ability to design a robust production pipeline for generating text embeddings and performing efficient, metadata-aware vector retrieval.
Machine LearningTechnical ScreenEmbeddingsVector DBData Pipelines - OpenAIMediumSoftware EngineerDebug a Machine Learning Pipeline
This question evaluates a candidate's ability to diagnose production machine learning failures, covering competencies in data quality, data and concept drift detection, model versioning and deployment checks, and operational debugging within an MLOps context.
Machine LearningTechnical Screen - OpenAIHardSoftware EngineerDebug a failing ML classifier
This question evaluates a candidate's ability to diagnose a poorly generalizing ML classifier, testing practical knowledge of data leakage, feature drift, and model calibration. It assesses applied machine learning debugging skills — including cross-validation design and error analysis — commonly tested in senior engineering and ML roles.
Machine LearningTechnical Screen - OpenAIMediumSoftware EngineerDesign Restart Strategy for Oracle Solver
This question tests the ability to optimize restart intervals for a randomized algorithm with heavy-tailed runtime distributions, balancing the trade-off between progress and computation waste.
Machine LearningHR ScreenProbabilistic ModelingAlgorithmsOptimization - OpenAIHardSoftware EngineerImplement a minimal reverse-mode autograd engine on PyTorch tensors
Implement the core of a PyTorch-style autograd system: a tensor wrapper that records addition, multiplication, matrix multiplication, ReLU and sum, plus a backward method that fills in gradients for every input. Tests reverse-mode differentiation, correct ordering of the backward pass, gradient accumulation for tensors used more than once, and verification against PyTorch.
Machine LearningTechnical Screen - OpenAIMediumSoftware EngineerImplement and Debug Backprop in NumPy
This question evaluates understanding of backpropagation and the chain rule, proficiency in vectorized NumPy implementation, numerical stability of softmax/cross-entropy, and competency in gradient checking and debugging of model gradients.
Machine LearningTechnical Screen - Qube Research & TechnologiesHardSoftware EngineerCompare bagging, boosting, random forests, and bias-variance
This question tests the theoretical understanding of ensemble learning mechanisms and their impact on the bias-variance decomposition of prediction error.
Machine LearningTechnical ScreenEnsemble LearningBias-VarianceRandom Forest - ReutersEasySoftware EngineerDiscuss overfitting, contrastive learning, transformers
This question evaluates understanding of supervised learning generalization (overfitting and underfitting), contrastive representation learning and contrastive loss functions, and transformer architectures, assessing core competencies in model evaluation, representation learning, and neural architecture concepts within the Machine Learning domain.
Machine LearningTechnical Screen - RipplingMediumSoftware EngineerFind minimum of unknown convex function
This question evaluates a candidate's understanding of convex optimization and zeroth-order (query-based) algorithm design, including reasoning about sample complexity, interval-reduction strategies, and robustness to noisy function evaluations.
Machine LearningTechnical Screen - SalesforceMediumSoftware EngineerPresent and Defend Recent Research on AI Agents
This question assesses the candidate's ability to communicate complex research, isolate their specific technical contributions, and synthesize academic concepts with practical AI-agent architectural challenges.
Machine LearningTechnical ScreenAI AgentsResearch CommunicationML Engineering - SquarepointMediumSoftware EngineerEvaluate Linear Regression Assumptions and Fit Three Points
Discuss the assumptions behind ordinary least squares, how multicollinearity affects a fitted model, and practical ways to address multicollinearity and overfitting. Cover data and labels, leakage-safe features, baselines and model choice, offline evaluation, deployment constraints, monitoring, and drift.
Machine LearningTechnical Screen - SunriseEasySoftware EngineerExplain the least squares method
Explain the least squares method 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 LearningOnsite - TeslaMediumSoftware EngineerHow to Identify Best Battery Group
This question evaluates understanding of statistical inference, experimental design, and metric construction for binary test outcomes, including defining composite quality measures, handling unequal sample sizes, quantifying uncertainty, and addressing multiple-comparison issues.
Machine LearningOnsite - TikTokMediumSoftware EngineerExplain DPO and construct its training data
This question evaluates understanding of Direct Preference Optimization (DPO) for fine-tuning large language models, assessing conceptual differences from PPO-based RLHF and the competency to design pairwise preference training datasets.
Machine LearningTechnical Screen - VisaMediumSoftware EngineerAssemble a RAG Pipeline From Provided Python Interfaces and Explain Retrieval
Assemble a retrieval-augmented generation pipeline from provided Python interfaces, covering chunking, embedding, vector search and grounded prompting, then explain why embeddings and cosine similarity work for retrieval and when RAG beats fine-tuning or long context. It tests applied LLM engineering and clear reasoning with unfamiliar tooling.
Machine LearningOnsite - WaymoMediumSoftware EngineerDesign an Online Experiment
This question tests the ability to design a statistically sound A/B test for a machine learning model update in a real-world, dynamic environment, focusing on metric selection and mitigation of interference.
Machine LearningOnsiteExperimentationA/B TestingCausal Inference - xAIMediumSoftware EngineerHow Would You Prevent Hallucinations in an LLM-Based System?
This question evaluates a candidate's understanding of hallucination in large language models, including underlying causes, detection, and system-level mitigation approaches, as well as their ability to reason about model reliability and failure modes.
Machine LearningHR Screen
Mistral AI
NVIDIA
OpenAI
Qube Research & Technologies
Rippling
Salesforce
Squarepoint
Tesla
TikTok
Visa
Waymo
xAI