ML System Design Interview Questions

Showing 121–138 of 138 results

• TTowerHard·Data Scientist
  Debug Oscillating GPU Utilization During Training

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

  ML System DesignTechnical Screendistributed-trainingml-system-designgpu-optimization
• TubiMedium·Machine Learning Engineer
  ML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)

  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.

  ML System DesignTechnical Screen
• TubiMedium·Machine Learning Engineer
  ML System Design: Thumbnail Selection for a Streaming Catalog

  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.

  ML System DesignTechnical Screen
• Two SigmaMedium·Software Engineer
  Design GenAI Fine-Tuning and Agent Tradeoffs

  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.

  ML System DesignTechnical ScreenLLM Fine-TuningAI AgentsModel Serving
• Two SigmaHard·Data Scientist
  Predict Citi Bike Demand at a Specific NYC Station

  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.

  ML System DesignTechnical Screen
• Two SigmaHard·Data Scientist
  Predicting Stock Prices from Twitter Data

  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.

  ML System DesignTechnical Screen
• UberMedium·Machine Learning Engineer
  Design a feed ranking system

  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.

  ML System DesignTechnical Screen
• UberMedium·Machine Learning Engineer
  Present a Marketplace ML Project Deep Dive

  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.

  ML System DesignTechnical Screen
• UberMedium·Machine Learning Engineer
  Where to apply ML in a task flow from customer request to driver-uploaded evidence

  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.

  ML System DesignTechnical Screen
• VoleonHard·Machine Learning Engineer
  Design a cold-start video recommender

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

  ML System DesignTechnical ScreenRecommender SystemsCold StartMulti-Armed Bandits
• VoleonHard·Machine Learning Engineer
  Estimate Fine-Tuning Memory Requirements

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

  ML System DesignTechnical Screen
• WayfairMedium·Applied Scientist
  Set Bids for Search Advertising from Business Value

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

  ML System DesignTechnical Screen
• WaymoMedium·Machine Learning Engineer
  Design Large-Scale Inference Serving

  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.

  ML System DesignTechnical Screen
• WhatnotMedium·Machine Learning Engineer
  Design trending livestream discovery

  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.

  ML System DesignTechnical Screen
• XPengHard·Machine Learning Engineer
  Design and train a PPO pipeline

  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.

  ML System DesignTechnical Screen
• XXometryMedium·Machine Learning Engineer
  Optimize Model Serving Under 200ms

  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.

  ML System DesignTechnical Screen
• EeBayEasy·Applied Scientist
  Design fast query-to-listing retrieval over tens of millions of listings

  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.

  ML System DesignTechnical Screen
• xAIHard·Machine Learning Engineer
  Implement a trie-based tokenizer

  This question evaluates a candidate's competency in designing and implementing a production-grade subword tokenizer, covering trie-based longest-prefix matching, Unicode-aware text processing, normalization and casing impacts, whitespace/punctuation rules, fallback strategies, performance and memory trade-offs, versioning, and testing for edge cases. It is commonly asked in ML System Design interviews to assess practical implementation skills and conceptual trade-off reasoning for deterministic, scalable LLM preprocessing, and tests both practical application and conceptual understanding within the ML System Design domain.

  ML System DesignTechnical Screen