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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 1–13 of 13 results

AmazonMediumSoftware Engineer
Brainstorm a business problem approach
Brainstorm a business problem approach evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen
AmazonMediumSoftware Engineer
Resolve a Conflict Between Metrics and Customer Feedback
This question tests your ability to troubleshoot conflicting signals in A/B testing and product analytics, requiring a structured approach to reconciling quantitative data with qualitative sentiment.
Analytics & ExperimentationOnsiteAnalyticsA/B TestingDecision Making
AnthropicHardSoftware Engineer
Design a profiling plan for kernels
This question evaluates competency in performance profiling, experimental design, microarchitectural analysis, statistical methodology, and kernel-level performance engineering.
Analytics & ExperimentationOnsite
AnthropicHardSoftware Engineer
How do you design an A/B experiment?
This problem tests your ability to design a rigorous A/B experiment for a consumer-facing AI product, focusing on identifying interference, selecting appropriate metrics, and addressing statistical validity.
Analytics & ExperimentationOnsiteA/B TestingExperiment DesignMetrics
AxonHardSoftware Engineer
Design an e-commerce analytics warehouse
The question evaluates data engineering and analytics competencies including event schema design, session and identity stitching, dimensional modeling, ingestion pipelines, and operational concerns such as deduplication, partitioning, and handling late or out‑of‑order events.
Analytics & ExperimentationTechnical Screen
GoogleHardSoftware Engineer
Design A/B testing platform
This question evaluates a candidate's competency in designing scalable, privacy-compliant A/B testing platforms, covering deterministic bucketing, traffic allocation, telemetry and event pipelines, experiment governance, and applied statistical methods in the Analytics & Experimentation domain.
Analytics & ExperimentationTechnical Screen
Graham CapitalMediumSoftware Engineer
Explain project assumptions and validation methods
This question evaluates expertise in analytics and experimentation, focusing on formulating project assumptions, selecting validation methods, and managing end-to-end data workflows and model evaluation.
Analytics & ExperimentationTechnical Screen
NVIDIAMediumSoftware Engineer
Define developer-centric usability metrics
This question evaluates proficiency in analytics and experimentation and the ability to define and operationalize developer-centric usability metrics for both consumer-facing applications and developer tools.
Analytics & ExperimentationTechnical Screen
OptiverHardSoftware Engineer
Design and backtest a trading strategy
This question evaluates a candidate's ability to design and rigorously evaluate a minute-level mean-reversion trading strategy, encompassing quantitative finance competencies such as signal construction, risk controls, position sizing, transaction-cost modeling, reproducible backtesting, and statistical validation; it belongs to the Analytics & Experimentation category within financial time-series and backtesting. It is commonly asked because it tests both practical implementation skills (reproducible backtests and walk-forward validation) and conceptual understanding (overfitting, data-snooping, and statistical significance testing), requiring a mix of practical application and conceptual statistical reasoning.
Analytics & ExperimentationTechnical Screen
OptiverMediumSoftware Engineer
Simulate return-weighted rebalancing strategy
Simulate return-weighted rebalancing strategy evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationTechnical Screen
RobloxHardSoftware Engineer
Define success metrics and monitoring
Define success metrics and monitoring evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite
UberMediumSoftware Engineer
Define and integrate room ranking factors
Define and integrate room ranking factors evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analytics & ExperimentationOnsite
VisaHardSoftware Engineer
Design an experiment assignment service
This question evaluates system-design and analytics competencies for online experimentation platforms in the Analytics & Experimentation domain, focusing on deterministic sticky assignment, configuration storage, eligibility targeting, traffic allocation, mutual exclusion, overrides, exposure logging, metric tagging, privacy/compliance, latency and availability targets, experiment lifecycle, and high-level architecture. It is commonly asked because it assesses both conceptual understanding and practical system-design application of distributed-system trade-offs, scalability, data integrity, operational reliability, and privacy constraints, and gauges the ability to define high-level APIs, architecture, and justify key trade-offs.
Analytics & ExperimentationTechnical Screen