Google interview questions

Showing 211–240 of 486 results

• Estimate population singletons from a 10% logMedium

  This question evaluates mastery of statistical estimation and sampling theory, including recognition of bias from subsampling, frequency‑of‑frequencies modeling for rare events, uncertainty quantification under heavy‑tailed counts, and design of simulation studies.

  GoogleData ScientistStatistics & MathTechnical Screen
• Estimate unbiased ad scores with many reviewersMedium

  This problem tests the ability to design an estimation framework that accounts for rater bias and variance, specifically applying concepts like hierarchical modeling or fixed-effect normalization to estimate latent variables.

  GoogleData ScientistStatistics & MathTechnical Screen
• Estimate weather’s effect on mental healthEasy

  This problem tests the ability to design an observational study to measure the causal impact of meteorological variables on self-reported mental health scores, focusing on confounding variables and identification strategies.

  GoogleData ScientistStatistics & MathHR Screen
• Evaluate AI Workflow Product MetricsHard

  Design an end-to-end measurement and experimentation framework for a generative AI workflow-suggestion tool. Tests metric taxonomy, funnel analytics, instrumentation pitfalls, and causal inference under interference.

  GoogleData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Auto-Reply Feature Success with Metrics and ExperimentsMedium

  Evaluates metrics and experimentation for an auto-reply suggestion feature in chat. Strong answers define adoption, latency, conversation quality, retention, and safety guardrails, design a causal experiment, and diagnose inconclusive results through funnel, segment, quality, and UI analyses.

  GoogleData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate Optimal Jogging Routes Feature with A/B TestingMedium

  Evaluates A/B test design for Google Maps jogging route recommendations. Strong answers define jogging intent and exposure, measure completed runs and satisfaction, include safety and privacy guardrails, and specify randomization, MDE, power, logging, and rollout criteria.

  GoogleData ScientistAnalytics & ExperimentationTechnical Screen
• Evaluate a Lisp-Style Expression Language with let, add and mult ScopingMedium

  A Google coding problem asking you to evaluate a small Lisp-like expression language with integer literals, variables, and add, mult and let forms. It tests tokenizing and parsing nested expressions, lexical scoping in which inner bindings shadow outer ones, and sequential rebinding inside a let.

  GoogleSoftware EngineerCoding & AlgorithmsOther
• Evaluate career paths under visa constraintsMedium

  This question evaluates decision-making, strategic career planning, immigration risk assessment, and trade-off analysis for a software engineer navigating multiple post-layoff options under visa constraints.

  GoogleSoftware EngineerBehavioral & LeadershipHR Screen
• Event Topic Router With Named Placeholders and Tail WildcardsHard

  Build a router for a telemetry message bus that maps dot-separated topics to registered subscription patterns with named placeholders and a trailing multi-segment wildcard; tests trie design, precedence rules and backtracking.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Evolve Duplicate Detection for Sliding Windows and Read-Heavy QueriesMedium

  Design a data structure that preprocesses an array in linear time to answer duplicate-within-distance-k queries in constant time.

  GoogleSoftware EngineerSoftware Engineering FundamentalsTechnical Screen
• Expand Recursive Key-Value Placeholders in a TemplateMedium

  Implement a template engine that recursively resolves variable placeholders using a key-value dictionary while handling circular dependencies, missing keys, and escaped characters.

  GoogleSoftware EngineerCoding & AlgorithmsTechnical Screen
• Experimentally evaluate jogging-route recommendationsHard

  This question evaluates competency in experimental design, metric selection and definition, statistical power/MDE calculation, contamination prevention, and operational monitoring for route recommendation features, targeted at Analytics & Experimentation for a Data Scientist role.

  GoogleData ScientistAnalytics & ExperimentationTechnical Screen
• Explain Bootstrap and Prove UniformityHard

  This question tests the conceptual understanding of bootstrap resampling for variance estimation and the mathematical proof of probability distributions using the properties of the Uniform distribution.

  GoogleData ScientistStatistics & MathTechnical Screen
• Explain Bootstrap and Statistical InferenceHard

  This problem tests your understanding of bootstrap resampling theory, confidence interval construction, and the statistical trade-offs involved in hypothesis testing for small-sample experiments.

  GoogleData ScientistStatistics & MathTechnical Screen
• Explain Changing Priorities and Your Role on a TeamMedium

  Use concrete examples to explain how you respond to a material priority change and what contribution teammates reliably seek from you. Strong responses show explicit trade-offs, stakeholder alignment, adaptable collaboration, measurable impact, honest limits, and lessons carried forward.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Explain GRPO-style training for diffusion modelsMedium

  This question tests your conceptual understanding of applying Group Relative Policy Optimization (GRPO) to diffusion models, focusing on how reinforcement learning can be used to steer image generation toward specific objective functions without explicit gradient feedback from the decoder.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Explain Importance Sampling in LLM TrainingHard

  Tests understanding of importance sampling in modern LLM fine-tuning, focusing on sequence-level likelihood ratios, exponential variance growth, support conditions, and practical mitigations like clipping and diagnostics.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Explain KL Divergence in Language ModelsHard

  Review KL divergence from its probability definition through directionality, support mismatch, sample estimates, and LLM regularization.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Explain LLM fine-tuning and generative modelsMedium

  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.

  GoogleSoftware EngineerMachine LearningTechnical Screen
• Explain LLM lifecycle and trade-offsMedium

  This question evaluates a candidate's understanding of the end-to-end lifecycle of large language models, covering training data collection and filtering, pretraining objectives, transformer architecture, post-training methods like supervised fine-tuning and preference optimization, and common model or training variants.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Explain ML model fundamentalsHard

  Explain ML model fundamentals 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.

  GoogleMachine Learning EngineerML System DesignOnsite
• Explain Motivation for an Experimental Technology TeamHard

  Craft a credible motivation answer for an experimental technology team by connecting mission interest with evidence-driven, responsible execution.

  GoogleFrontend EngineerBehavioral & LeadershipOnsite
• Explain P-values and Confidence Intervals in Simple Linear RegressionMedium

  Review p-values, confidence intervals and slope inference in simple linear regression, including assumptions and causal limits.

  GoogleData ScientistStatistics & MathOnsite
• Explain Pipeline Parallelism in GRPO TrainingHard

  Design and analyze an efficient pipeline parallel training architecture for Group Relative Policy Optimization (GRPO) on large reasoning models. Tests deep understanding of pipeline scheduling, microbatching, distributed advantage normalization, and policy staleness trade-offs.

  GoogleMachine Learning EngineerML System DesignTechnical Screen
• Explain SLI/SLO/SLA and design monitoringHard

  This question evaluates a candidate's competency in service reliability and observability, specifically understanding and distinguishing SLIs, SLOs, and SLAs, planning error budgets, and designing monitoring and alerting for a production web API.

  GoogleSoftware EngineerSoftware Engineering FundamentalsOnsite
• Explain Simpson’s Paradox and Its Causes with ExampleMedium

  Evaluates Simpson's paradox through definition, confounding, subgroup imbalance, and a numeric reversal example. Strong answers compare stratified and aggregate rates and explain when each comparison matters.

  GoogleData ScientistStatistics & MathTechnical Screen
• Explain VAEs, ELBO, KL, and ReparameterizationHard

  Derive and explain the theoretical foundations of Variational Autoencoders (VAEs), linking the Evidence Lower Bound (ELBO) to network architecture, gradient estimation via the reparameterization trick, and posterior collapse.

  GoogleMachine Learning EngineerMachine LearningTechnical Screen
• Explain What You Would Do DifferentlyMedium

  This question assesses a candidate's ability to demonstrate professional growth, self-awareness, and the capacity to synthesize past mistakes into improved engineering practices.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Explain Your Most Meaningful Project ContributionMedium

  Prepare a project story that makes your most meaningful contribution and personal ownership clear. Explain one consequential decision, its tradeoffs and obstacles, how you evaluated the outcome, and why the work still matters.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite
• Explain Your Most Technically Complex ProjectMedium

  This question assesses the candidate's ability to articulate complex technical challenges, justify architectural design decisions, and communicate the impact of their personal contributions in a collaborative environment.

  GoogleSoftware EngineerBehavioral & LeadershipOnsite