ML System Design interview questions

Showing 301–330 of 332 results

• Design query generation to maximize CTRHard

  Design query generation to maximize CTR 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.

  TikTokMachine Learning EngineerML System DesignTechnical Screen
• Design system to detect privacy-leak recordsMedium

  This question evaluates a candidate's ability to design scalable ML-driven systems for detecting and classifying privacy-sensitive and PII-containing records across structured and unstructured data, testing competencies in data engineering, machine learning (including deep learning and LLM/RAG integrations), system architecture, and privacy/security considerations. It is commonly asked to probe reasoning about functional and non-functional requirements, trade-offs in detection and classification approaches, evaluation metrics like precision and recall, feedback loops and operational scaling, and it falls under ML system design with a practical, application-level focus rather than purely conceptual abstraction.

  TikTokSoftware EngineerML System DesignTechnical Screen
• Design video captioning under compute limitsMedium

  This question evaluates expertise in ML system design for multimodal large models, covering deployment under compute and GPU memory constraints as well as large-scale retrieval and processing of video captions and embeddings.

  TikTokMachine Learning EngineerML System DesignTechnical Screen
• Debug Oscillating GPU Utilization During TrainingHard

  Design a systematic debugging and architectural remediation strategy for a distributed deep learning training cluster exhibiting severe GPU utilization oscillation between 90% and 20%.

  TowerData ScientistML System DesignTechnical Screen
• ML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)Medium

  This question tests ML system design competency, specifically the ability to architect end-to-end recommendation pipelines covering retrieval, ranking, feature engineering, and low-latency serving at scale. It assesses practical knowledge of two-stage recommender systems, implicit feedback modeling, cold-start handling, and critical evaluation of AI-generated code — skills central to machine learning engineering interviews.

  TubiMachine Learning EngineerML System DesignTechnical Screen
• ML System Design: Thumbnail Selection for a Streaming CatalogMedium

  This ML system design question tests a candidate's ability to architect an end-to-end personalization and ranking system, covering problem framing, reward modeling, and low-latency serving at scale. It evaluates practical knowledge of contextual bandits, engagement-signal design, cold-start handling, and off-policy evaluation — core competencies for machine learning engineering roles.

  TubiMachine Learning EngineerML System DesignTechnical Screen
• Design GenAI Fine-Tuning and Agent TradeoffsMedium

  Design an enterprise GenAI research agent platform, evaluating trade-offs between fine-tuning methods (Full, LoRA, QLoRA) versus RAG, alongside agent orchestration, structured outputs, and safety controls.

  Two SigmaSoftware EngineerML System DesignTechnical Screen
• Predict Citi Bike Demand at a Specific NYC StationHard

  This question evaluates time-series forecasting, demand estimation, and ML system-design competencies, focusing on problem framing, handling censored observations, data integration, model selection, and evaluation under operational constraints.

  Two SigmaData ScientistML System DesignTechnical Screen
• Predicting Stock Prices from Twitter DataHard

  This question evaluates competency in ML system design for integrating NLP-derived signals from social media with time-series financial modeling, including feature extraction, timestamp alignment, and handling low signal-to-noise ratios.

  Two SigmaData ScientistML System DesignTechnical Screen
• Design a feed ranking systemMedium

  This question evaluates a candidate's competency in designing end-to-end machine-learning-based feed ranking systems, including setting ranking objectives, candidate generation and filtering, feature design, model training and serving, and operational concerns like freshness, diversity, and scalability.

  UberMachine Learning EngineerML System DesignTechnical Screen
• Design a restaurant recommendation systemMedium

  An Uber MLE onsite ML system design question: design a personalized restaurant recommendation system for a food delivery marketplace's home feed. It tests candidate generation and ranking, feature engineering and training data, cold-start and exploration, low-latency serving, and offline/online evaluation, plus marketplace concerns like availability, ETA, diversity, fairness, and business rules.

  UberMachine Learning EngineerML System DesignOnsite
• Design real-time grid ETA for driversHard

  This question evaluates a candidate's competency in real-time ML system design, covering streaming data ingestion, GPS noise handling and denoising, online feature computation, low-latency model serving, and operational monitoring.

  UberMachine Learning EngineerML System DesignOnsite
• Present a Marketplace ML Project Deep DiveMedium

  This question evaluates a candidate's competence in end-to-end ML system design, product-oriented machine learning, and cross-functional skills such as feature engineering, model deployment, online experimentation, monitoring, stakeholder communication, and measuring business impact.

  UberMachine Learning EngineerML System DesignTechnical Screen
• Where to apply ML in a task flow from customer request to driver-uploaded evidenceMedium

  An ML system design discussion about a task platform: walk the end-to-end flow from a customer submitting a task to a driver completing it and uploading evidence, and identify where machine learning and LLMs add value. Tests prioritization, model framing, labels, evaluation and risk handling.

  UberMachine Learning EngineerML System DesignTechnical Screen
• Design a Feature Store for Offline Training and Low-Latency Online ServingMedium

  Design a feature store that ingests high-volume event data, produces point-in-time correct training sets, and serves the latest features at low latency in production. It tests streaming and batch feature computation, leakage-free historical joins, online store design, and keeping training and serving consistent.

  UnitySoftware EngineerML System DesignOnsite
• Review and Debug an AI ChatbotMedium

  Design an end-to-end debugging, observability, and evaluation architecture for a multi-tenant enterprise AI compliance chatbot experiencing hallucinations, latency spikes, and policy violations.

  VantaSoftware EngineerML System DesignOnsite
• Design a cold-start video recommenderHard

  This problem tests the ability to architect a recommendation system that provides personalized content for new users or items with zero historical interaction data.

  VoleonMachine Learning EngineerML System DesignTechnical Screen
• Estimate Fine-Tuning Memory RequirementsHard

  Learn to estimate full and parameter-efficient fine-tuning memory with a transparent ledger for model state, activations, sharding, and peak overhead.

  VoleonMachine Learning EngineerML System DesignTechnical Screen
• Design a Conversational Agent for Finding Vacation RentalsEasy

  Design a multi-turn assistant that turns a traveler's free-form trip description into structured, grounded rental search results. Tests where to use an LLM versus deterministic search, clarification strategy, safety against hallucination and injection, and evaluation against classic search.

  Walmart LabsMachine Learning EngineerML System DesignOnsite
• Design an Agentic Search ExperienceEasy

  Design an agentic search experience with clarifying dialogue, structured constraints, grounded retrieval, bounded tool use, and evaluation against conventional search.

  Walmart LabsMachine Learning EngineerML System DesignOnsite
• Design an item category prediction systemEasy

  This question tests the ability to design an end-to-end classification system for assigning retail items into a hierarchical category structure, focusing on feature selection, model architecture, and production deployment.

  Walmart LabsMachine Learning EngineerML System DesignOnsite
• Rank Homepage Modules for an E-Commerce ProductMedium

  Rank e-commerce homepage modules using business objectives, seasonal eligibility, slot effects, slate diversity, cold-start exploration, and page-level experiments.

  WayfairApplied ScientistML System DesignOnsite
• Set Bids for Search Advertising from Business ValueMedium

  Design search-ad bidding from expected business value, with budget pacing, delayed conversion feedback, auction assumptions, and incremental-profit evaluation.

  WayfairApplied ScientistML System DesignTechnical Screen
• Design Large-Scale Inference ServingMedium

  This question evaluates understanding of large-scale ML inference systems, assessing competencies in capacity planning, latency and tail-latency engineering, memory and bandwidth estimation, hardware selection (CPUs/GPUs/specialized accelerators), batching and caching trade-offs, and reliability concerns such as out-of-memory prevention and recovery. It is commonly asked to test practical system-design skills for production deployment by requiring back-of-the-envelope QPS and resource estimates and reasoning about operational trade-offs; this belongs to the ML system design category and emphasizes practical application over purely conceptual theory.

  WaymoMachine Learning EngineerML System DesignTechnical Screen
• Design a Hybrid Evaluation PlatformMedium

  This problem tests your ability to design a multi-modal evaluation infrastructure that balances automated LLM-based grading with human-in-the-loop (HITL) verification to ensure high-fidelity ML model performance metrics.

  WaymoSoftware EngineerML System DesignOnsite
• Design trending livestream discoveryMedium

  This question evaluates expertise in designing end-to-end ML-driven recommendation systems, including competencies in real-time feature engineering, candidate generation, model serving, business-rule integration, monitoring, experimentation, and operational trade-offs for live content.

  WhatnotMachine Learning EngineerML System DesignTechnical Screen
• Design AI-Powered Document SearchMedium

  This question assesses the ability to design an end-to-end, scalable pipeline for ingesting, processing, and indexing heterogeneous documents (PDFs, images, text files) to support AI-driven semantic search.

  WorkdaySoftware EngineerML System DesignOnsite
• Design and train a PPO pipelineHard

  This question evaluates proficiency in designing and training Proximal Policy Optimization (PPO) pipelines, covering environment interfacing, observation/action design, reward shaping, rollout and advantage estimation, hyperparameterization, normalization, parallelization, evaluation, and sim‑to‑real considerations.

  XPengMachine Learning EngineerML System DesignTechnical Screen
• Optimize Model Serving Under 200msMedium

  This question evaluates competency in deploying and optimizing machine learning models for low-latency online inference, covering model serving, latency profiling, hardware considerations, and managing accuracy–latency trade-offs within a 200ms SLO in the ML System Design domain.

  XometryMachine Learning EngineerML System DesignTechnical Screen
• Design fast query-to-listing retrieval over tens of millions of listingsEasy

  An ML system design question on retrieving relevant listings for free-text search queries across a catalog of tens of millions of items with low latency. It tests candidate generation, indexing, embedding training, freshness, filtering and scaling, plus the offline and A/B test metrics used to evaluate retrieval.

  eBayApplied ScientistML System DesignTechnical Screen