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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
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Showing 1–30 of 36 results
- AmazonMediumSoftware EngineerExplain overfitting, regularization, and LLM techniques
This question tests the conceptual understanding of model generalization trade-offs, the mathematical mechanics of regularization, and the practical application of modern LLM architectures.
Machine LearningTechnical ScreenRegularizationLLMRAG - AmazonHardSoftware EngineerUse a Fitted Line to Predict a Future Data Point
This question tests the ability to implement a simple linear regression predictor and analyze the reliability of extrapolating time-series data using basic statistical heuristics.
Machine LearningTechnical ScreenRegressionTime SeriesStatistics - AncestryMediumSoftware EngineerExplain learning paradigms, loss, and embeddings
This question evaluates understanding of core machine learning concepts—learning paradigms (supervised, unsupervised, self-supervised), loss functions, and embeddings—and the competency to explain their roles and interrelations in model training and inference.
Machine LearningTechnical Screen - AnthropicMediumSoftware EngineerDebug a GRPO training loop and explain ratios
This question evaluates debugging and implementation knowledge for on-policy reinforcement learning, focusing on GRPO/PPO-style training loops, importance-sampling ratios, log-prob computations, masking, and advantage normalization.
Machine LearningTechnical Screen - ByteDanceMediumSoftware EngineerChoose Precision, Recall, or F1 for Agent Evaluation
This question tests the ability to map business objectives and error costs to appropriate classification metrics in a real-world system context.
Machine LearningTechnical ScreenMetricsClassificationModel Evaluation - CrestaMediumSoftware EngineerDesign sequence decoding with greedy and beam search
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Design sequence decoding with greedy and beam search states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Machine LearningTechnical Screen - DeshawHardSoftware EngineerExplain and Compare SGD and Adam
Explain how stochastic gradient descent updates model parameters and how Adam modifies those updates using moving estimates of first and second gradient moments. Cover data and labels, leakage-safe features, baselines and model choice, offline evaluation, deployment constraints, monitoring, and drift.
Machine LearningTechnical Screen - GoogleMediumSoftware EngineerExplain LLM fine-tuning and generative models
This question tests your conceptual understanding of generative model architectures and the practical trade-offs involved in adapting large-scale language models to specific tasks.
Machine LearningTechnical ScreenLLMFine-tuningGenerative Models - GoogleMediumSoftware EngineerHow Would You Verify a Smiling-Face Detection Feature in Google Photos?
A role-related question for software engineers: if your feature detects smiling faces in Google Photos, how would you verify that the detection is correct? It tests defining ground truth, choosing evaluation metrics and thresholds, covering hard cases and demographic slices, and validating the feature before and after launch.
Machine LearningTechnical Screen - IntuitMediumSoftware EngineerExplain LLMs, AI-native vs. conventional apps, daily AI use, and the dangers of AI
Staff-level backend interview discussion on AI fluency. Explain what a large language model is, contrast AI-native products with conventional apps that add AI features, describe how you use AI tools in daily engineering work, and weigh the main dangers of AI with concrete mitigations.
Machine LearningTechnical Screen - IntuitEasySoftware EngineerWhen should products use AI?
This question evaluates product-oriented decision-making about AI adoption, testing competencies in machine learning product strategy, trade-off analysis (accuracy, latency, cost, explainability, safety, and user trust), and the ability to distinguish AI-native architectures from conventional feature additions.
Machine LearningTechnical Screen - Jump TradingHardSoftware EngineerAccelerate Tree-Model Inference
Design an end-to-end strategy to accelerate inference latency and throughput for an existing gradient-boosted decision tree (GBDT) model in a low-latency trading environment while preserving exact prediction fidelity. Address feature engineering constraints, low-level execution optimizations, verification of numerical equivalence, and production drift monitoring.
Machine LearningTechnical ScreenTree ModelsInference OptimizationLow-Latency Systems - MicrosoftMediumSoftware EngineerGreedy and Top-k Beam Search Decoding That Returns the Sentence Probability
A coding question on decoding a sequence from a next-token probability distribution: implement greedy decoding, extend it to beam search that keeps the top k partial sequences, and return the chosen sequence with its overall probability. It also probes why long sequences tend to receive low probability and how decoding should account for that.
Machine LearningTechnical Screen - MillenniumMediumSoftware EngineerHow would you model stock price prediction?
This problem tests the candidate's ability to design an end-to-end machine learning pipeline for financial time-series forecasting, specifically focusing on the nuances of feature engineering and evaluation in a non-stationary environment.
Machine LearningTechnical ScreenTime-SeriesFeature EngineeringFinancial Engineering - Mistral AIMediumSoftware EngineerCompare RMSNorm and LayerNorm in Transformer Blocks
Compare LayerNorm and RMSNorm equations, centering, shift behavior, hidden-feature axes, numerical precision, and the tradeoffs behind simpler normalization.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerCompare Reward-Model RLHF and Direct Preference Optimization
Compare reward-model RLHF and DPO through preference data, policy objectives, reference regularization, training stages, and evaluation under distribution shift.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerCompare Tensor Parallelism and Pipeline Parallelism
Compare transformer tensor and pipeline parallelism through parameter partitioning, collectives, activation transfers, microbatch bubbles, memory, and inference latency.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerExplain Adam, AdamW, and Decoupled Weight Decay
Understand Adam moment estimates and bias correction, then compare gradient-based L2 regularization with AdamW’s decoupled weight decay and learning-rate interaction.
Machine LearningTechnical Screen - Mistral AIMediumSoftware EngineerExplain Mixture of Experts and Expert Parallelism
Explain sparse mixture-of-experts routing and expert parallelism, including token dispatch, weighted combination, load balancing, overflow policies, and communication costs.
Machine LearningTechnical Screen - 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 - 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
Amazon
Anthropic
Cresta
Google
Intuit
Microsoft
Millennium
Mistral AI
NVIDIA
OpenAI