ML System Design Interview Questions

Showing 61–90 of 332 results

• AtlassianMedium·Machine Learning Engineer
  Design a Jira+Confluence RAG assistant

  This question evaluates competencies in designing retrieval-augmented generation systems, including ingestion and indexing, retrieval and ranking strategies, prompt-based generation and citation handling, permission-aware access control, and monitoring for freshness and latency.

  ML System DesignOnsite
• AtlassianHard·Machine Learning Engineer
  Design an ML-powered search system

  This question tests the ability to design a scalable, low-latency, and relevant search system for internal enterprise documents, focusing on the integration of hybrid search (keyword + semantic) and ranking strategies.

  ML System DesignOnsiteInformation RetrievalRankingEmbeddings
• AtlassianMedium·Machine Learning Engineer
  Design an internal data-access chatbot

  This question evaluates a candidate's ability to design production-ready ML systems that integrate retrieval-augmented generation, secure access control, tool-based workflows, data privacy, auditability, and low-latency interaction.

  ML System DesignOnsite
• AuroraHard·Machine Learning Engineer
  Applying LLMs to Autonomous Driving Despite Hallucination and Real-Time Limits

  An open-ended ML system design discussion on how large language models and vision-language models could be used in autonomous driving, including whether one could actually drive the vehicle. It tests mapping off-board and on-vehicle uses, containing hallucination and latency in a safety-critical stack, and planning evaluation.

  ML System DesignTechnical Screen
• BasetenHard·Software Engineer
  Design an LLM Inference Serving System

  Design an end-to-end, high-performance LLM serving platform that manages request admission, continuous batching, KV cache memory, token streaming, and GPU fleet autoscaling under tight latency SLAs.

  ML System DesignTechnical Screenml-system-designllm-servingdistributed-systems
• BByteDanceMedium·Machine Learning Engineer
  Design Self-Dealing Detection for Marketplaces

  This question evaluates competency in machine learning system design for fraud detection, focusing on modeling buyer–seller–item–transaction relationships, feature engineering across graph, temporal, behavioral and account-linking signals, and production concerns; it is in the ML System Design domain and emphasizes practical application and system-level architecture over purely theoretical concepts. It is commonly asked to assess a candidate’s ability to reason about data collection and labeling, model and rule trade-offs, evaluation metrics, scalability and deployment trade-offs when detecting coordinated or self-dealing activity in marketplace environments.

  ML System DesignTechnical Screen
• BByteDanceMedium·Software Engineer
  Design a Content Moderation Platform

  This problem evaluates your ability to design a scalable, multi-modal content moderation system that filters user-uploaded media using both automated ML inferences and human review workflows.

  ML System DesignOnsiteSystem DesignMLOpsScalability
• BByteDanceMedium·Software Engineer
  Design a RAG-Based Agent System

  This question tests the ability to architect a scalable Retrieval-Augmented Generation (RAG) system that handles document indexing, retrieval, and multi-step reasoning while maintaining low latency and data freshness.

  ML System DesignTechnical ScreenRAGLLMSystem Design
• BByteDanceMedium·Software Engineer
  Design a Short-Video Recommendation System

  This problem tests the ability to design a multi-stage recommendation pipeline that balances real-time personalization, system latency, and content diversity.

  ML System DesignOnsiteSystem DesignRecommendation SystemsScalability
• BByteDanceMedium·Senior SDE, Quality Platform & AI Test Automation
  Design an Agent-Orchestrated QA Pipeline from Requirements to Test Report

  Design an end-to-end multi-agent QA platform that autonomously transforms feature requirements into validated test cases, synthetic data, executable test scripts, and traceable execution reports with human-in-the-loop oversight.

  ML System DesignTechnical ScreenAI AgentsTest AutomationCI/CD
• BByteDanceMedium·Machine Learning Engineer
  Design an Enterprise Tool-Using Agent

  This question evaluates a candidate's ability to design production-grade LLM agents that integrate external tools, manage long-running and branching workflows, persist complex state, and ensure safety, observability, and reliable evaluation in enterprise settings.

  ML System DesignTechnical Screen
• C3 AIHard·Data Scientist
  Evaluate a Retrieval-Augmented Generation System

  Design an end-to-end evaluation framework for an enterprise RAG system that assesses retrieval quality, generative fidelity, claim-level attribution, security controls, and production telemetry.

  ML System DesignTechnical ScreenRAGLLM EvaluationSystem Design
• C3 AIHard·Data Scientist
  Forecast Food Stocking Needs Under Waste and Stockout Costs

  Design an end-to-end inventory forecasting and stocking optimization system for perishable food items under asymmetric costs of spoilage versus stockouts. The design covers handling censored sales data, probabilistic demand modeling, cost-optimal stocking policies, and validation strategy.

  ML System DesignTechnical Screenml-system-designtime-seriessupply-chain
• C3 AIHard·Data Scientist
  Reduce Fresh-Food Waste with an End-to-End ML Approach

  This problem tests the ability to design an integrated ML system that moves beyond simple demand forecasting by addressing inventory dynamics, supply constraints, and decision optimization.

  ML System DesignOnsiteSupply ChainOptimizationCensored Data
• Capital OneMedium·Machine Learning Engineer
  Design model deployment, monitoring, and low-latency inference

  This question evaluates competency in ML system design and production engineering—covering model deployment and versioning, safe rollouts and rollbacks, monitoring of service health, data quality/drift, model performance and business metrics, and latency optimization for low-latency inference.

  ML System DesignOnsite
• Capital OneMedium·Data Scientist
  Explain an ML Project from Model Development Through Deployment

  This question assesses your ability to articulate the end-to-end lifecycle of an ML project, focusing on the bridge between technical modeling choices and real-world business impact.

  ML System DesignTechnical ScreenML LifecycleModel DeploymentSystem Design
• CitadelHard·Data Scientist
  Build a regression model for wind power output

  This question evaluates competency in feature engineering, physics-informed regression modeling, sensor-data quality handling, model selection and validation, and uncertainty estimation for turbine-level instantaneous power prediction.

  ML System DesignOnline Assessment
• CitadelHard·Software Engineer
  Build models for housing and wind power prediction

  This question evaluates ML system design and applied modeling competencies, including data assumptions, preprocessing, feature engineering, model selection, validation strategies, temporal leakage handling, and end-to-end pipeline construction for a binary housing-affordability classifier and a time-series regression for wind-farm power prediction.

  ML System DesignOnline Assessment
• CitadelHard·Data Scientist
  Design a time-series home-buy decision classifier

  This question evaluates competency in ML system design for time-series decisioning, including rigorous target and horizon definition, temporal feature engineering, time-aware validation, probabilistic calibration and thresholding, leakage controls, model selection trade-offs, and deployment monitoring.

  ML System DesignOnline Assessment
• CitadelMedium·Data Scientist
  Stabilize LLM inference and estimate needed repeats

  This question tests the ability to design an LLM inference pipeline that ensures result stability through statistical aggregation and estimates the minimum number of inference samples required to achieve a target confidence level.

  ML System DesignTechnical ScreenLLMInferenceStatistics
• CCognitivMedium·Machine Learning Engineer
  Design a Real-Time Feature Store

  This question evaluates a candidate's ability to design real-time feature store architectures, encompassing competencies in low-latency serving, point-in-time correctness for offline training, streaming ingestion, consistency between online and offline features, handling backfills and late events, and scalable monitoring of data quality and reliability. It is commonly asked to assess systems-design and data-engineering judgment for production-grade ML infrastructure, falls under the ML system design and data engineering domain, and tests both conceptual understanding of trade-offs (consistency, latency) and practical application skills for APIs, storage, and operational design.

  ML System DesignTechnical Screen
• CohereMedium·Machine Learning Engineer
  Design How an LLM Detects Questions Beyond Its Training Cutoff and Says So

  Design how a large language model recognizes questions that depend on information after its training cutoff and tells the user where its knowledge ends. The question tests data construction for calibrated abstention, prompt versus fine-tuning versus retrieval trade-offs, and evaluating both over-refusal and hallucination.

  ML System DesignOnsite
• CohereMedium·Machine Learning Engineer
  Design a Post-Training Pipeline to Improve a 7B Model's Coding for an Enterprise

  Design an end-to-end post-training pipeline that improves a 7B language model's coding ability for an enterprise customer, covering data collection, SFT, RLHF, evaluation, serving and a production feedback loop. The question probes dataset construction, reward model choice and why RLHF is still needed when SFT can learn from preference data.

  ML System DesignOnsite
• CohereMedium·Machine Learning Engineer
  Improve LLM reasoning for a domain task

  This problem tests your ability to design a robust pipeline for an LLM-based system that requires multi-step reasoning in a specialized domain (e.g., technical legal analysis or medical diagnosis).

  ML System DesignOnsiteLLMReasoningRAG
• CCreditkarmaMedium·Machine Learning Engineer
  Design Personalized Promotion Recommendations

  This question tests the ability to design an end-to-end recommendation system for personalized financial promotions, focusing on handling sparse conversion data, real-time feature requirements, and feedback loops.

  ML System DesignTechnical ScreenRecommendation SystemsRankingFeature Engineering
• CCreditkarmaMedium·Machine Learning Engineer
  Design a Revenue Ranking Platform

  This problem tests your ability to design a multi-stage ranking system that balances business revenue goals with user relevance, specifically addressing sparse conversion data and long-term user retention.

  ML System DesignOnsiteRankingRecommendationMulti-task Learning
• CrestaMedium·Software Engineer
  Design an Evaluation Platform for AI Agents Across Prompt and Model Changes

  An ML system design question about a platform that lets teams evaluate AI agents and measure how a change of system prompt or model affects quality, tool use and response speed. It tests evaluation design, scoring with references and LLM judges, statistical comparison of versions and scalable execution.

  ML System DesignOnsite
• CCriblMedium·Software Engineer
  Design an LLM Log Parsing Workflow

  This question evaluates skills in ML-enabled log parsing, schema inference, structured data extraction, and designing scalable, reliable production workflows that combine probabilistic LLMs with deterministic parsers and operational engineering.

  ML System DesignTechnical Screen
• DRWMedium·Machine Learning Engineer
  Build pipeline for imbalanced classification

  This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Build pipeline for imbalanced classification states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

  ML System DesignOnline Assessment
• DRWHard·Machine Learning Engineer
  Train LinearSVC to beat a hidden baseline

  Train LinearSVC to beat a hidden baseline 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.

  ML System DesignOnline Assessment