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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 1–12 of 12 results
- AMDMediumSoftware EngineerExplain the ReAct Agent Loop
Explain a ReAct tool loop through state, action selection, observations, recovery, stopping criteria, and validation of task completion.
Machine LearningOnsite - AmazonMediumSoftware EngineerEvaluating Generative AI Use and Accuracy
Tests a candidate's ability to assess when Generative AI is the right solution, choose appropriate architecture patterns (such as RAG or fine-tuning), and rigorously measure and improve model output accuracy in production.
Machine LearningOnsiteGenerative AIRAGML Evaluation - AmazonMediumSoftware EngineerExplain Decoder-Only LLM Architecture and How vLLM Serves It Efficiently
A two-part conversational question for AI infrastructure roles: explain how a decoder-only transformer turns a prompt into next-token probabilities, then explain how vLLM serves such models efficiently. It tests transformer internals, KV cache sizing, prefill versus decode bottlenecks, paged KV memory and continuous batching.
Machine LearningOnsite - AnthropicHardSoftware EngineerImplement and analyze custom attention
This question evaluates implementation and analysis skills for scaled dot-product attention, testing competencies in efficient tensorized PyTorch coding, numerical stability and mixed-precision handling, masking semantics (causal and padding), multi-head shape correctness, and unit-test validation including gradient and edge-case behavior.
Machine LearningOnsite - AnthropicMediumSoftware EngineerImplement and derive backprop from scratch
This question evaluates understanding and practical implementation of neural network fundamentals, specifically analytic backpropagation and gradient derivation, numerically stable binary cross-entropy computation, parameter initialization, gradient descent updates, and gradient checking.
Machine LearningOnsite - ByteDanceMediumSoftware EngineerHand-Write a Multi-Head Attention Layer: Projections, Head Splitting and Masking
Write a multi-head attention layer from scratch: project queries, keys and values, split them into heads, apply scaled dot-product attention with optional padding or causal masks, and merge the heads. It tests tensor shape discipline, numerical stability, and the cost of attention as sequence length grows.
Machine LearningOnsite - HarveyMediumSoftware EngineerEmbedding Retrieval with Cosine Similarity in a Notebook, and Choosing the Metric
Implement embedding-based document retrieval with cosine similarity in a Python notebook, then explain how cosine similarity relates to dot product and Euclidean distance and how to measure retrieval quality. It tests vectorized top-k search, deterministic ties, index metric choice and evaluation metrics.
Machine LearningOnsite - Hudson River TradingHardSoftware EngineerBuild a Heart Disease Baseline
This question evaluates proficiency in applied machine learning workflows, including data inspection and cleaning, exploratory data analysis, feature engineering, baseline binary classification modeling, and evaluation using Python libraries like pandas and seaborn.
Machine LearningOnsite - 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 - 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
Amazon
Anthropic
Harvey
Hudson River Trading
Tesla
Visa
Waymo